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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 (18 January 2024) | Viewed by 22674

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Dr. Muhammad Attique Khan

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Guest Editor

Department of Computer Science, HITEC University, Taxila 47040, Pakistan
Interests: medical image analysis

Prof. Dr. Yu-Dong Zhang

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Guest Editor

Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: artificial intelligence; deep learning; medical image processingrecognition; transfer learning; medical image analysis
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Dear Colleagues,

Machine-learning-based approaches are attracting a lot of attention due to the wide range of applications in various fields. The last two decades have witnessed increasing interest in computer-aided medical systems for early detection, diagnosis, prognosis, risk assessment, and final therapy of diseases. The development of a reliable medical solution is a crucial task because there is no single standard approach covering all the subdomains, including data processing, regions of interest detection, image segmentation and registration, image fusion, and classification with high accuracy. Therefore, computer-aided diagnosis systems are still a highly challenging domain which provides enough space for improvement. These days, deep-learning-based methods are attracting much attention among researchers in the machine learning community due to their improved segmentation and classification results. Moreover, deep-learning-based methods have also lowered the barriers of data preprocessing and extreme set of users’ dependability. Consequently, the processing burden in medical imaging has now shifted from the human to the computer side, thus allowing more researchers to step into this well-regarded and momentous area. This leads to improved performance, both in terms of accuracy and decision time.

This Special Issue seeks high-quality research articles generally dealing with methods such as semantic segmentation and deep learning in the field of medical image processing. We are primarily interested in original research articles proposing novel solutions, covering new theories, and describing new implementations for medical image analytics.

Dr. Muhammad Attique Khan
Prof. Dr. Yu-Dong Zhang
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

deep-learning-based segmentation
federated learning
semantic segmentation for medical infection diagnosis
wireless capsule endoscopy (WCE) imaging technology using deep learning
magnetic resonance imaging (MRI)
semantic techniques for MRI images
FPGA with deep learning for medical imaging
mammogram imaging modality using deep learning
ultrasound imaging modality detection using deep learning
X-ray computed tomography (CT)
deep-learning-based CAD systems
transfer learning in deep learning for medical imaging
cancers classification using deep learning
autoencoder-based features selection using deep learning in the medical field
fusion of convolutional layers in deep learning for recognition
optimal deep learning features selection for recognition
fusion of image modality using deep learning
deep-learning-based medical imaging retrieval

Published Papers (10 papers)

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Research

16 pages, 2014 KiB

Open AccessArticle

Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images

by Glenn Linde, Renoh Chalakkal, Lydia Zhou, Joanna Lou Huang, Ben O’Keeffe, Dhaivat Shah, Scott Davidson and Sheng Chiong Hong

Diagnostics 2023, 13(17), 2810; https://doi.org/10.3390/diagnostics13172810 - 30 Aug 2023

Viewed by 1961

Abstract

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model’s performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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16 pages, 2820 KiB

Open AccessArticle

Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease

by Po-Fan Chiu, Robert Chen-Hao Chang, Yung-Chi Lai, Kuo-Chen Wu, Kuan-Pin Wang, You-Pen Chiu, Hui-Ru Ji, Chia-Hung Kao and Cheng-Di Chiu

Diagnostics 2023, 13(11), 1863; https://doi.org/10.3390/diagnostics13111863 - 26 May 2023

Cited by 1 | Viewed by 1539

Abstract

Background: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)–based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. Methods: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. Results: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. Conclusions: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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30 pages, 14007 KiB

Open AccessArticle

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features

by Ibrahim Abdulrab Ahmed, Ebrahim Mohammed Senan and Hamzeh Salameh Ahmad Shatnawi

Diagnostics 2023, 13(10), 1758; https://doi.org/10.3390/diagnostics13101758 - 16 May 2023

Cited by 2 | Viewed by 1506

Abstract

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease’s characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN–FFNN and CNN–XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet–MobileNet–DenseNet121–XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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26 pages, 7185 KiB

Open AccessArticle

Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

by M. Lakshmi and Raja Das

Diagnostics 2023, 13(9), 1639; https://doi.org/10.3390/diagnostics13091639 - 05 May 2023

Cited by 6 | Viewed by 1799

Abstract

In this research, we demonstrate a Deep Convolutional Neural Network-based classification model for the detection of monkeypox. Monkeypox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles in symptoms. The early diagnosis of monkeypox helps doctors cure it more quickly. Therefore, pre-trained models are frequently used in the diagnosis of monkeypox, because the manual analysis of a large number of images is labor-intensive and prone to inaccuracy. Therefore, finding the monkeypox virus requires an automated process. The large layer count of convolutional neural network (CNN) architectures enables them to successfully conceptualize the features on their own, thereby contributing to better performance in image classification. The scientific community has recently articulated significant attention in employing artificial intelligence (AI) to diagnose monkeypox from digital skin images due primarily to AI’s success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal). The majority of images in our research are collected from publicly available datasets. This study suggests an adaptive k-means clustering image segmentation technique that delivers precise segmentation results with straightforward operation. Our preliminary computational findings reveal that the proposed model could accurately detect patients with monkeypox. The best overall accuracy achieved by ResNet101 is 94.25%, with an AUC of 98.59%. Additionally, we describe the categorization of our model utilizing feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which provides a more in-depth understanding of particular properties that distinguish the monkeypox virus. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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19 pages, 32347 KiB

Open AccessArticle

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

by Saravanan Srinivasan, Subathra Gunasekaran, Sandeep Kumar Mathivanan, Prabhu Jayagopal, Muhammad Attique Khan, Areej Alasiry, Mehrez Marzougui and Anum Masood

Diagnostics 2023, 13(8), 1385; https://doi.org/10.3390/diagnostics13081385 - 10 Apr 2023

Cited by 1 | Viewed by 1559

Abstract

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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16 pages, 2548 KiB

Open AccessArticle

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

by Farah Mohammad, Saad Al Ahmadi and Jalal Al Muhtadi

Diagnostics 2023, 13(7), 1229; https://doi.org/10.3390/diagnostics13071229 - 24 Mar 2023

Cited by 2 | Viewed by 2363

Abstract

Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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21 pages, 4965 KiB

Open AccessArticle

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

by Ahmed A. Abd El-Latif, Samia Allaoua Chelloug, Maali Alabdulhafith and Mohamed Hammad

Diagnostics 2023, 13(7), 1216; https://doi.org/10.3390/diagnostics13071216 - 23 Mar 2023

Cited by 11 | Viewed by 3915

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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15 pages, 1895 KiB

Open AccessArticle

End-to-End Deep Learning Method for Detection of Invasive Parkinson’s Disease

by Awais Mahmood, Muhammad Mehroz Khan, Muhammad Imran, Omar Alhajlah, Habib Dhahri and Tehmina Karamat

Diagnostics 2023, 13(6), 1088; https://doi.org/10.3390/diagnostics13061088 - 13 Mar 2023

Cited by 4 | Viewed by 1656

Abstract

Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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25 pages, 5577 KiB

Open AccessArticle

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

by Swaminathan Sundaram, Meganathan Selvamani, Sekar Kidambi Raju, Seethalakshmi Ramaswamy, Saiful Islam, Jae-Hyuk Cha, Nouf Abdullah Almujally and Ahmed Elaraby

Diagnostics 2023, 13(5), 1001; https://doi.org/10.3390/diagnostics13051001 - 06 Mar 2023

Cited by 4 | Viewed by 2120

Abstract

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person’s growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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19 pages, 1040 KiB

Open AccessArticle

Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI

by Suvita Rani Sharma, Samah Alshathri, Birmohan Singh, Manpreet Kaur, Reham R. Mostafa and Walid El-Shafai

Diagnostics 2023, 13(5), 925; https://doi.org/10.3390/diagnostics13050925 - 01 Mar 2023

Cited by 6 | Viewed by 2200

Abstract

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images. Full article

(This article belongs to the Special Issue Medical Image Processing and Analysis)

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