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Diagnostics
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Deep Disease Detection and Diagnosis Models
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Deep Disease Detection and Diagnosis Models

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 40315

Special Issue Editors

Dr. Dilbag Singh

E-Mail Website
Guest Editor
NYU Grossman School of Medicine, New York University, New York, NY, USA
Interests: automated disease diagnosis; deep learning; machine learning; lightweight models; disease segmentation; federated learning; explainable AI
Special Issues, Collections and Topics in MDPI journals
Dr. Vijay Kumar

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, National Institute of Technology, Hamirpur 177005, Himachal Pradesh, India
Interests: optimization; deep learning; machine learning; restoration
Special Issues, Collections and Topics in MDPI journals
Dr. Dinesh Kumar

E-Mail Website
Guest Editor
Department of Electronic Engineering, Delhi Technological University, New Delhi 110042, Delhi, India
Interests: optimization; deep learning; machine learning; restoration; lightweight models; disease segmentation; federated learning; explainable AI

Special Issue Information

Dear Colleagues, 

A number of clinical trials have shown that the early and accurate detection of disease can reduce mortality rates and ensure the effective treatment of patients. Over the past decades, machine learning models have been widely utilized for the early detection and diagnosis of several diseases. However, machine learning models require handcrafted features for training and testing purposes. To overcome this problem, deep learning models have been developed. These models can extract the potential features of multimodal medical data automatically with the help of convolution layers. Fully connected layers and activation functions are then applied to the extracted features for classification purposes. Recently, the performance of deep learning models for the early diagnosis and detection of various diseases has significantly improved. However, these models suffer from a number of serious drawbacks, such as gradient vanishing, over-fitting, hyperparameter tuning, and extensive computation. To overcome these problems, researchers have designed various approaches, such as evolving and compressing existing deep learning models. However, this is still an open area of research. Therefore, the main objective of this Special Issue is to consider novel articles and review articles that can overcome the aforementioned problems of deep learning models, and build effective disease detection and diagnosis frameworks.

Dr. Dilbag Singh
Dr. Vijay Kumar
Dr. Dinesh Kumar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automated disease diagnosis
  • deep learning
  • machine learning
  • lightweight models
  • disease segmentation
  • federated learning
  • explainable AI
  • medical Internet of Things
  • remote diagnosis

Published Papers (15 papers)

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Research

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19 pages, 707 KiB  
Article
DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images
by Manjit Kaur, Ahmad Ali AlZubi, Arpit Jain, Dilbag Singh, Vaishali Yadav and Ahmed Alkhayyat
Diagnostics 2023, 13(17), 2752; https://doi.org/10.3390/diagnostics13172752 - 24 Aug 2023
Cited by 3 | Viewed by 1016
Abstract
Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such [...] Read more.
Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback–Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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15 pages, 937 KiB  
Article
Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network
by Faizan Ullah, Muhammad Nadeem, Mohammad Abrar, Muna Al-Razgan, Taha Alfakih, Farhan Amin and Abdu Salam
Diagnostics 2023, 13(16), 2650; https://doi.org/10.3390/diagnostics13162650 - 11 Aug 2023
Cited by 5 | Viewed by 2358
Abstract
Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of [...] Read more.
Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method’s performance. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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29 pages, 9146 KiB  
Article
Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier
by Kavita Behara, Ernest Bhero and John Terhile Agee
Diagnostics 2023, 13(16), 2635; https://doi.org/10.3390/diagnostics13162635 - 09 Aug 2023
Cited by 7 | Viewed by 1833
Abstract
The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies [...] Read more.
The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed well in medical diagnosis. Unfortunately, such models require a substantial amount of annotated data for training. Applying a data augmentation method based on generative adversarial networks (GANs) to classify skin lesions is a plausible solution by generating synthetic images to address the problem. This article proposes a skin lesion synthesis and classification model based on an Improved Deep Convolutional Generative Adversarial Network (DCGAN). The proposed system generates realistic images using several convolutional neural networks, making training easier. Scaling, normalization, sharpening, color transformation, and median filters enhance image details during training. The proposed model uses generator and discriminator networks, global average pooling with 2 × 2 fractional-stride, backpropagation with a constant learning rate of 0.01 instead of 0.0002, and the most effective hyperparameters for optimization to efficiently generate high-quality synthetic skin lesion images. As for the classification, the final layer of the Discriminator is labeled as a classifier for predicting the target class. This study deals with a binary classification predicting two classes—benign and malignant—in the ISIC2017 dataset: accuracy, recall, precision, and F1-score model classification performance. BAS measures classifier accuracy on imbalanced datasets. The DCGAN Classifier model demonstrated superior performance with a notable accuracy of 99.38% and 99% for recall, precision, F1 score, and BAS, outperforming the state-of-the-art deep learning models. These results show that the DCGAN Classifier can generate high-quality skin lesion images and accurately classify them, making it a promising tool for deep learning-based medical image analysis. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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23 pages, 5864 KiB  
Article
Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods
by Mohammed Zakariah and Yousef Ajami Alotaibi
Diagnostics 2023, 13(14), 2323; https://doi.org/10.3390/diagnostics13142323 - 10 Jul 2023
Cited by 4 | Viewed by 1459
Abstract
Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps [...] Read more.
Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps or calories expended. Due to the patient’s inability to obtain the necessary information to understand their conditions and detect illness, such as depression, objectively, methods for evaluating various mental disorders, such as the Montgomery–Asberg depression rating scale (MADRS) and observations, currently require a significant amount of effort on the part of specialists. In this study, a novel dataset was provided, comprising sensor data gathered from depressed patients. The dataset included 32 healthy controls and 23 unipolar and bipolar depressive patients with motor activity recordings. Along with the sensor data collected over several days of continuous measurement for each patient, some demographic information was also offered. The result of the experiment showed that less than 70 of the 100 epochs of the model’s training were completed. The Cohen Kappa score did not even pass 0.1 in the validation set, due to an imbalance in the class distribution, whereas in the second experiment, the majority of scores peaked in about 20 epochs, but because training continued during each epoch, it took much longer for the loss to decline before it fell below 0.1. In the second experiment, the model soon reached an accuracy of 0.991, which is as expected given the outcome of the UMAP dimensionality reduction. In the last experiment, UMAP and neural networks worked together to produce the best outcomes. They used a variety of machine learning classification algorithms, including the nearest neighbors, linear kernel SVM, Gaussian process, and random forest. This paper used the UMAP unsupervised machine learning dimensionality reduction without the neural network and showed a slightly lower score (QDA). By considering the ratings of the patient’s depressive symptoms that were completed by medical specialists, it is possible to better understand the relationship between depression and motor activity. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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40 pages, 8976 KiB  
Article
Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques
by Amel Ksibi, Mohammed Zakariah, Leila Jamel Menzli, Oumaima Saidani, Latifah Almuqren and Rosy Awny Mohamed Hanafieh
Diagnostics 2023, 13(10), 1779; https://doi.org/10.3390/diagnostics13101779 - 17 May 2023
Cited by 8 | Viewed by 5827
Abstract
The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given [...] Read more.
The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient’s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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22 pages, 2446 KiB  
Article
Brain Tumor Segmentation Using Deep Learning on MRI Images
by Almetwally M. Mostafa, Mohammed Zakariah and Eman Abdullah Aldakheel
Diagnostics 2023, 13(9), 1562; https://doi.org/10.3390/diagnostics13091562 - 27 Apr 2023
Cited by 6 | Viewed by 3929
Abstract
Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have [...] Read more.
Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model’s output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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23 pages, 5250 KiB  
Article
Intelligent Wireless Capsule Endoscopy for the Diagnosis of Gastrointestinal Diseases
by Ibrahim M. Mehedi, K. Prahlad Rao, Fahad Mushhabbab Alotaibi and Hadi Mohsen Alkanfery
Diagnostics 2023, 13(8), 1445; https://doi.org/10.3390/diagnostics13081445 - 17 Apr 2023
Cited by 5 | Viewed by 2205
Abstract
Through a wireless capsule endoscope (WCE) fitted with a miniature camera (about an inch), this study aims to examine the role of wireless capsule endoscopy (WCE) in the diagnosis, monitoring, and evaluation of GI (gastrointestinal) disorders. In a wearable belt recorder, a capsule [...] Read more.
Through a wireless capsule endoscope (WCE) fitted with a miniature camera (about an inch), this study aims to examine the role of wireless capsule endoscopy (WCE) in the diagnosis, monitoring, and evaluation of GI (gastrointestinal) disorders. In a wearable belt recorder, a capsule travels through the digestive tract and takes pictures. It attempts to find tiny components that can be used to enhance the WCE. To accomplish this, we followed the steps below: Researching current capsule endoscopy through databases, designing and simulating the device using computers, implanting the system and finding tiny components compatible with capsule size, testing the system and eliminating noise and other problems, and analyzing the results. In the present study, it was shown that a spherical WCE shaper and a smaller WCE with a size of 13.5 diameter, a high resolution, and a high frame rate (8–32 fps) could help patients with pains due to the traditional capsules and provide more accurate pictures as well as prolong the battery life. In addition, the capsule can also be used to reconstruct 3D images. Simulation experiments showed that spherical endoscopic devices are more advantageous than commercial capsule-shaped endoscopic devices for wireless applications. We found that the sphere’s velocity through the fluid was greater than the capsule’s. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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19 pages, 5018 KiB  
Article
Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning
by Jamil Ahmad, Abdul Khader Jilani Saudagar, Khalid Mahmood Malik, Muhammad Badruddin Khan, Abdullah AlTameem, Mohammed Alkhathami and Mozaherul Hoque Abul Hasanat
Diagnostics 2023, 13(8), 1387; https://doi.org/10.3390/diagnostics13081387 - 11 Apr 2023
Cited by 1 | Viewed by 1429
Abstract
The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited [...] Read more.
The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited data is more critical than ever before. To allow prediction with limited COVID-19 data as a case study, we present a complete framework for progression and prognosis prediction in chest X-rays (CXR) through reasoning in a COVID-specific deep feature space. The proposed approach relies on a pre-trained deep learning model that has been fine-tuned specifically for COVID-19 CXRs to identify infection-sensitive features from chest radiographs. Using a neuronal attention-based mechanism, the proposed method determines dominant neural activations that lead to a feature subspace where neurons are more sensitive to COVID-related abnormalities. This process allows the input CXRs to be projected into a high-dimensional feature space where age and clinical attributes like comorbidities are associated with each CXR. The proposed method can accurately retrieve relevant cases from electronic health records (EHRs) using visual similarity, age group, and comorbidity similarities. These cases are then analyzed to gather evidence for reasoning, including diagnosis and treatment. By using a two-stage reasoning process based on the Dempster–Shafer theory of evidence, the proposed method can accurately predict the severity, progression, and prognosis of a COVID-19 patient when sufficient evidence is available. Experimental results on two large datasets show that the proposed method achieves 88% precision, 79% recall, and 83.7% F-score on the test sets. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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19 pages, 11044 KiB  
Article
A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer
by Ricky Walsh and Mickael Tardy
Diagnostics 2023, 13(1), 67; https://doi.org/10.3390/diagnostics13010067 - 26 Dec 2022
Cited by 11 | Viewed by 2453
Abstract
Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples [...] Read more.
Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy cases, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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19 pages, 4393 KiB  
Article
An Ensemble of Transfer Learning Models for the Prediction of Skin Cancers with Conditional Generative Adversarial Networks
by Amal Al-Rasheed, Amel Ksibi, Manel Ayadi, Abdullah I. A. Alzahrani, Mohammed Zakariah and Nada Ali Hakami
Diagnostics 2022, 12(12), 3145; https://doi.org/10.3390/diagnostics12123145 - 13 Dec 2022
Cited by 4 | Viewed by 1623
Abstract
Skin cancer is one of the most severe forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, diagnosing and treating skin cancer patients at an early stage is crucial. Since a manual skin [...] Read more.
Skin cancer is one of the most severe forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, diagnosing and treating skin cancer patients at an early stage is crucial. Since a manual skin cancer diagnosis is both time-consuming and expensive, an incorrect diagnosis is made due to the high similarity between the various skin cancers. Improved categorization of multiclass skin cancers requires the development of automated diagnostic systems. Herein, we propose a fully automatic method for classifying several skin cancers by fine-tuning the deep learning models VGG16, ResNet50, and ResNet101. Prior to model creation, the training dataset should undergo data augmentation using traditional image transformation techniques and Generative Adversarial Networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a realistic appearance using Conditional Generative Adversarial Network (CGAN) techniques. Thereafter, the traditional augmentation methods are used to augment our existing training set to improve the performance of pre-trained deep models on the skin cancer classification task. This improved performance is then compared to the models developed using the unbalanced dataset. In addition, we formed an ensemble of finely tuned transfer learning models, which we trained on balanced and unbalanced datasets. These models were used to make predictions about the data. With appropriate data augmentation, the proposed models attained an accuracy of 92% for VGG16, 92% for ResNet50, and 92.25% for ResNet101, respectively. The ensemble of these models increased the accuracy to 93.5%. A comprehensive discussion on the performance of the models concluded that using this method possibly leads to enhanced performance in skin cancer categorization compared to the efforts made in the past. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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25 pages, 1780 KiB  
Article
A Proposed Framework for Early Prediction of Schistosomiasis
by Zain Ali, Muhammad Faisal Hayat, Kamran Shaukat, Talha Mahboob Alam, Ibrahim A. Hameed, Suhuai Luo, Shakila Basheer, Manel Ayadi and Amel Ksibi
Diagnostics 2022, 12(12), 3138; https://doi.org/10.3390/diagnostics12123138 - 12 Dec 2022
Cited by 9 | Viewed by 2089
Abstract
Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in [...] Read more.
Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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20 pages, 3578 KiB  
Article
A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm
by Deepti Verma, Shweta Agrawal, Celestine Iwendi, Bhisham Sharma, Surbhi Bhatia and Shakila Basheer
Diagnostics 2022, 12(11), 2643; https://doi.org/10.3390/diagnostics12112643 - 31 Oct 2022
Cited by 4 | Viewed by 1448
Abstract
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during [...] Read more.
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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25 pages, 3579 KiB  
Article
Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
by Riaz Ullah Khan, Sultan Almakdi, Mohammed Alshehri, Rajesh Kumar, Ikram Ali, Sardar Muhammad Hussain, Amin Ul Haq, Inayat Khan, Aman Ullah and Muhammad Irfan Uddin
Diagnostics 2022, 12(10), 2539; https://doi.org/10.3390/diagnostics12102539 - 19 Oct 2022
Cited by 9 | Viewed by 1638
Abstract
The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on [...] Read more.
The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach’s alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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16 pages, 2345 KiB  
Article
An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset
by Talha Mahboob Alam, Kamran Shaukat, Waseem Ahmad Khan, Ibrahim A. Hameed, Latifah Abd. Almuqren, Muhammad Ahsan Raza, Memoona Aslam and Suhuai Luo
Diagnostics 2022, 12(9), 2115; https://doi.org/10.3390/diagnostics12092115 - 31 Aug 2022
Cited by 57 | Viewed by 6173
Abstract
Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier [...] Read more.
Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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Review

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31 pages, 6885 KiB  
Review
Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions
by Saransh Bhachawat, Eashwar Shriram, Kathiravan Srinivasan and Yuh-Chung Hu
Diagnostics 2023, 13(2), 288; https://doi.org/10.3390/diagnostics13020288 - 12 Jan 2023
Cited by 3 | Viewed by 2120
Abstract
Degenerative nerve diseases such as Alzheimer’s and Parkinson’s diseases have always been a global issue of concern. Approximately 1/6th of the world’s population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The [...] Read more.
Degenerative nerve diseases such as Alzheimer’s and Parkinson’s diseases have always been a global issue of concern. Approximately 1/6th of the world’s population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient’s medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases. Full article
(This article belongs to the Special Issue Deep Disease Detection and Diagnosis Models)
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