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Artificial Intelligence in Medical Image Processing and Disease Diagnosis

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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 (16 January 2024) | Viewed by 12027

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Dr. Chiranji Lal Chowdhary

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Guest Editor

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Interests: image processing; computer vision; pattern recognition; machine learning; biometric systems; deep learning; soft computing; computational intelligence

Dr. Vijay Kumar

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Guest Editor

Department of Computer Science and Engineering, National Institute of Technology, Hamirpur 177005, Himachal Pradesh, India
Interests: optimization; deep learning; machine learning; restoration
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Special Issue Information

Dear Colleagues,

The healthcare system is a dynamic and changing environment that continually faces new challenges with changing responsibilities and frequent interruptions for medical specialists.

Applications of artificial intelligence (AI) offer benefits in terms of disease diagnostics. Due to its low cost, lack of infrastructure, equipment, and special needs, the artificial intelligence-based medical diagnostic is so important. Additionally, AI-based medical diagnostics reduce diagnostic time and increase accuracy noticeably. Machine learning and deep learning AI techniques have a key role in enhancing new clinical systems, patient information and records, and the treatment of various ailments, among other health-related topics. The diagnosis of multiple diseases most effectively using AI approaches.

This Special Issue aims to explore how artificial intelligence techniques provide a knowledge to contribute to both original research work and review articles. This Special Issue needs to address medical imaging areas with the successful application of AI in disease diagnostics with the latest research directions of machine learning, deep learning, federated learning, metaverse, and digital twins.

Dr. Chiranji Lal Chowdhary
Dr. Vijay 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

artificial intelligence techniques
medical diagnosis
medical image processing
deep learning
machine learning
computer vision
healthcare informatics
electronic patient information and health records
natural language processing
clinical systems
illness treatment
federated learning
digital twins

Published Papers (9 papers)

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Research

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21 pages, 945 KiB

Open AccessArticle

An Integrated Machine Learning Approach for Congestive Heart Failure Prediction

by M. Sheetal Singh, Khelchandra Thongam, Prakash Choudhary and P. K. Bhagat

Diagnostics 2024, 14(7), 736; https://doi.org/10.3390/diagnostics14070736 - 29 Mar 2024

Viewed by 554

Abstract

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew’s correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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25 pages, 6368 KiB

Open AccessArticle

PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs

by Sivashankari Rajadurai, Kumaresan Perumal, Muhammad Fazal Ijaz and Chiranji Lal Chowdhary

Diagnostics 2024, 14(5), 469; https://doi.org/10.3390/diagnostics14050469 - 21 Feb 2024

Viewed by 577

Abstract

Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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15 pages, 4918 KiB

Open AccessArticle

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images

by Zhi-Hui Xu, Da-Ge Fan, Jian-Qiang Huang, Jia-Wei Wang, Yi Wang and Yuan-Zhe Li

Diagnostics 2023, 13(24), 3669; https://doi.org/10.3390/diagnostics13243669 - 14 Dec 2023

Viewed by 969

Abstract

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201’s performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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18 pages, 3410 KiB

Open AccessArticle

Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry

by Madalina Maria Diac, Gina Madalina Toma, Simona Irina Damian, Marin Fotache, Nicolae Romanov, Daniel Tabian, Gabriela Sechel, Andrei Scripcaru, Monica Hancianu and Diana Bulgaru Iliescu

Diagnostics 2023, 13(24), 3630; https://doi.org/10.3390/diagnostics13243630 - 08 Dec 2023

Viewed by 874

Abstract

Background: Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person’s sex based only on the morphometry of L1–L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine. Method: This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric. Results: The L1–L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model. Conclusions: Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person’s sex based only on the L1–L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1–L5 measurements of a body/cadaver, they can predict the person’s sex. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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16 pages, 4944 KiB

Open AccessArticle

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

by Oğuz Kaya and Burak Taşcı

Diagnostics 2023, 13(21), 3317; https://doi.org/10.3390/diagnostics13213317 - 26 Oct 2023

Viewed by 797

Abstract

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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19 pages, 521 KiB

Open AccessArticle

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

by Sujit Bebortta, Subhranshu Sekhar Tripathy, Shakila Basheer and Chiranji Lal Chowdhary

Diagnostics 2023, 13(20), 3166; https://doi.org/10.3390/diagnostics13203166 - 10 Oct 2023

Cited by 5 | Viewed by 1223

Abstract

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal–dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant’s private medical information. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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21 pages, 4143 KiB

Open AccessArticle

AI Evaluation of Imaging Factors in the Evolution of Stage-Treated Metastases Using Gamma Knife

by Calin G. Buzea, Razvan Buga, Maria-Alexandra Paun, Madalina Albu, Dragos T. Iancu, Bogdan Dobrovat, Maricel Agop, Viorel-Puiu Paun and Lucian Eva

Diagnostics 2023, 13(17), 2853; https://doi.org/10.3390/diagnostics13172853 - 04 Sep 2023

Viewed by 1094

Abstract

Background: The study investigated whether three deep-learning models, namely, the CNN_model (trained from scratch), the TL_model (transfer learning), and the FT_model (fine-tuning), could predict the early response of brain metastases (BM) to radiosurgery using a minimal pre-processing of the MRI images. The dataset consisted of 19 BM patients who underwent stereotactic-radiosurgery (SRS) within 3 months. The images used included axial fluid-attenuated inversion recovery (FLAIR) sequences and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences from the tumor center. The patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). Methods: A total of 2320 images from the regression class and 874 from the progression class were randomly assigned to training, testing, and validation groups. The DL models were trained using the training-group images and labels, and the validation dataset was used to select the best model for classifying the evaluation images as showing regression or progression. Results: Among the 19 patients, 15 were classified as “responders” and 4 as “non-responders”. The CNN_model achieved good performance for both classes, showing high precision, recall, and F1-scores. The overall accuracy was 0.98, with an AUC of 0.989. The TL_model performed well in identifying the “progression” class, but could benefit from improved precision, while the “regression” class exhibited high precision, but lower recall. The overall accuracy of the TL_model was 0.92, and the AUC was 0.936. The FT_model showed high recall for “progression”, but low precision, and for the “regression” class, it exhibited a high precision, but lower recall. The overall accuracy for the FT_model was 0.83, with an AUC of 0.885. Conclusions: Among the three models analyzed, the CNN_model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images. This suggests that CNN models could potentially predict SRS prognoses from small datasets. However, further analysis is needed, especially in cases where class imbalances exist. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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15 pages, 4178 KiB

Open AccessArticle

Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm

by Dayananda Pruthviraja, Sowmyarani C. Nagaraju, Niranjanamurthy Mudligiriyappa, Mahesh S. Raisinghani, Surbhi Bhatia Khan, Nora A. Alkhaldi and Areej A. Malibari

Diagnostics 2023, 13(16), 2687; https://doi.org/10.3390/diagnostics13162687 - 15 Aug 2023

Cited by 2 | Viewed by 1231

Abstract

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer’s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer’s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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30 pages, 1407 KiB

Open AccessSystematic Review

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

by Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Ahmed M. Dinar and Begonya Garcia Zapirain

Diagnostics 2023, 13(4), 664; https://doi.org/10.3390/diagnostics13040664 - 10 Feb 2023

Cited by 4 | Viewed by 2428

Abstract

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Image Processing and Disease Diagnosis)

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