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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 (30 November 2023) | Viewed by 17560

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Dr. Kawsar Ahmed

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Guest Editor

Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
Interests: biomedical engineering; biophotonics; biosensor; machine learning; federated learning; health informatics

Dr. Francis Bui

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Guest Editor

Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
Interests: information processing; machine learning; federated learning; biomedical engineering; health informatics; communication

Dr. Mohammad Ali Moni

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Guest Editor

Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: statistical models; machine learning; deep learning; medical images; neuroimaging; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer is one of the most common and leading causes of death throughout the world. The World Health Organization (WHO) estimates that around 10 million people die of cancer each year, which represents one sixth of the total deaths around the world. The most common types of cancers are breast cancer, lung cancer, prostate cancer, leukemia, colon cancer, skin cancer, stomach cancer, etc. There are a lot of reasons behind the spread of cancer among people, such as smoking, high body mass index, alcohol consumption, and lack of physical activity. In addition to that, 30% of the total cancer cases are caused by human papillomavirus (HPV), hepatitis, and other cancer-causing infections.

Early detection of cancer can be a potential solution to reduce the mortality rate. Early treatment by detecting cancer at an early stage increases the probability of survival. Early detection can be performed in two ways: through screening tests and diagnosis. Screening tests are very expensive, which discourages people from lower- and middle-income countries. Thus, a deep learning approach can be a potential solution for the early diagnosis of cancer. This issue aims to collect novel deep learning approaches to detect any kinds of cancers at an early stage.

Dr. Kawsar Ahmed
Dr. Francis Bui
Dr. Mohammad Ali Moni
Guest Editors

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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

deep learning
cancer diagnosis
image processing
early detection

Published Papers (9 papers)

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Research

15 pages, 21455 KiB

Open AccessArticle

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging

by Reza Kalantar, Sebastian Curcean, Jessica M. Winfield, Gigin Lin, Christina Messiou, Matthew D. Blackledge and Dow-Mu Koh

Diagnostics 2023, 13(21), 3381; https://doi.org/10.3390/diagnostics13213381 - 03 Nov 2023

Viewed by 909

Abstract

T₂-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T₂W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595–0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568–0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T₂W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model’s ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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18 pages, 1656 KiB

Open AccessArticle

ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector

by Nitha V. R. and Vinod Chandra S. S.

Diagnostics 2023, 13(13), 2206; https://doi.org/10.3390/diagnostics13132206 - 29 Jun 2023

Cited by 2 | Viewed by 2013

Abstract

Lung cancer is an abnormality where the body’s cells multiply uncontrollably. The disease can be deadly if not detected in the initial stage. To address this issue, an automated lung cancer malignancy detection (ExtRanFS) framework is developed using transfer learning. We used the IQ-OTH/NCCD dataset gathered from the Iraq Hospital in 2019, encompassing CT scans of patients suffering from various lung cancers and healthy subjects. The annotated dataset consists of CT slices from 110 patients, of which 40 were diagnosed with malignant tumors and 15 with benign tumors. Fifty-five patients were determined to be in good health. All CT images are in DICOM format with a 1mm slice thickness, consisting of 80 to 200 slices at various sides and angles. The proposed system utilized a convolution-based pre-trained VGG16 model as the feature extractor and an Extremely Randomized Tree Classifier as the feature selector. The selected features are fed to the Multi-Layer Perceptron (MLP) Classifier for detecting whether the lung cancer is benign, malignant, or normal. The accuracy, sensitivity, and F1-Score of the proposed framework are 99.09%, 98.33%, and 98.33%, respectively. To evaluate the proposed model, a comparison is performed with other pre-trained models as feature extractors and also with the existing state-of-the-art methodologies as classifiers. From the experimental results, it is evident that the proposed framework outperformed other existing methodologies. This work would be beneficial to both the practitioners and the patients in identifying whether the tumor is benign, malignant, or normal. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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19 pages, 3306 KiB

Open AccessArticle

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

by Adel Sulaiman, Swapandeep Kaur, Sheifali Gupta, Hani Alshahrani, Mana Saleh Al Reshan, Sultan Alyami and Asadullah Shaikh

Diagnostics 2023, 13(12), 2121; https://doi.org/10.3390/diagnostics13122121 - 20 Jun 2023

Cited by 3 | Viewed by 1716

Abstract

Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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27 pages, 9172 KiB

Open AccessArticle

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron

by Md. Tarek Aziz, S. M. Hasan Mahmud, Md. Fazla Elahe, Hosney Jahan, Md Habibur Rahman, Dip Nandi, Lassaad K. Smirani, Kawsar Ahmed, Francis M. Bui and Mohammad Ali Moni

Diagnostics 2023, 13(12), 2106; https://doi.org/10.3390/diagnostics13122106 - 18 Jun 2023

Cited by 4 | Viewed by 2611

Abstract

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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18 pages, 3996 KiB

Open AccessArticle

SCDet: A Robust Approach for the Detection of Skin Lesions

by Shahbaz Sikandar, Rabbia Mahum, Adham E. Ragab, Sule Yildirim Yayilgan and Sarang Shaikh

Diagnostics 2023, 13(11), 1824; https://doi.org/10.3390/diagnostics13111824 - 24 May 2023

Cited by 3 | Viewed by 1554

Abstract

Red, blue, white, pink, or black spots with irregular borders and small lesions on the skin are known as skin cancer that is categorized into two types: benign and malignant. Skin cancer can lead to death in advanced stages, however, early detection can increase the chances of survival of skin cancer patients. There exist several approaches developed by researchers to identify skin cancer at an early stage, however, they may fail to detect the tiniest tumours. Therefore, we propose a robust method for the diagnosis of skin cancer, namely SCDet, based on a convolutional neural network (CNN) having 32 layers for the detection of skin lesions. The images, having a size of 227 × 227, are fed to the image input layer, and then pair of convolution layers is utilized to withdraw the hidden patterns of the skin lesions for training. After that, batch normalization and ReLU layers are used. The performance of our proposed SCDet is computed using the evaluation matrices: precision 99.2%; recall 100%; sensitivity 100%; specificity 99.20%; and accuracy 99.6%. Moreover, the proposed technique is compared with the pre-trained models, i.e., VGG16, AlexNet, and SqueezeNet and it is observed that SCDet provides higher accuracy than these pre-trained models and identifies the tiniest skin tumours with maximum precision. Furthermore, our proposed model is faster than the pre-trained model as the depth of its architecture is not too high as compared to pre-trained models such as ResNet50. Additionally, our proposed model consumes fewer resources during training; therefore, it is better in terms of computational cost than the pre-trained models for the detection of skin lesions. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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15 pages, 848 KiB

Open AccessArticle

Identifying Effective Feature Selection Methods for Alzheimer’s Disease Biomarker Gene Detection Using Machine Learning

by Hala Alshamlan, Samar Omar, Rehab Aljurayyad and Reham Alabduljabbar

Diagnostics 2023, 13(10), 1771; https://doi.org/10.3390/diagnostics13101771 - 17 May 2023

Cited by 1 | Viewed by 1404

Abstract

Alzheimer’s disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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16 pages, 17475 KiB

Open AccessArticle

A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation

by Chandra Sekhara Rao Annavarapu, Samson Anosh Babu Parisapogu, Nikhil Varma Keetha, Praveen Kumar Donta and Gurindapalli Rajita

Diagnostics 2023, 13(8), 1406; https://doi.org/10.3390/diagnostics13081406 - 13 Apr 2023

Cited by 2 | Viewed by 1558

Abstract

Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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33 pages, 10152 KiB

Open AccessArticle

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

by Ibrahim Abdulrab Ahmed, Ebrahim Mohammed Senan, Hamzeh Salameh Ahmad Shatnawi, Ziad Mohammad Alkhraisha and Mamoun Mohammad Ali Al-Azzam

Diagnostics 2023, 13(6), 1026; https://doi.org/10.3390/diagnostics13061026 - 08 Mar 2023

Cited by 10 | Viewed by 2132

Abstract

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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12 pages, 5485 KiB

Open AccessArticle

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

by Sreejith Vidyadharan, Budhiraju Veera Venkata Satya Naga Prabhakar Rao, Yogeeswari Perumal, Kesavadas Chandrasekharan and Venkateswaran Rajagopalan

Diagnostics 2022, 12(12), 3216; https://doi.org/10.3390/diagnostics12123216 - 19 Dec 2022

Cited by 7 | Viewed by 2002

Abstract

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor’s texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently. Full article

(This article belongs to the Special Issue Deep Learning for Early Detection of Cancer)

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