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Diagnostics
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Artificial Intelligence in Biomedical Image Analysis—2nd Edition
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Artificial Intelligence in Biomedical Image Analysis—2nd Edition

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 October 2024) | Viewed by 4312

Special Issue Editors

Prof. Dr. Ayman El-Baz

E-Mail Website
Guest Editor
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: biosignaling; bioimaging modeling and computer-assisted functional diagnostic diagnosis systems, including those using CT, MRI, EMG, ECG, EEG, and other physiological signals
Special Issues, Collections and Topics in MDPI journals
Dr. Jasjit S. Suri

E-Mail Website
Guest Editor
1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The word “smart”, used frequently in our daily life, is usually associated with some kind of machine learning. Whether we are talking about a smart device or a smart app, machine learning is playing a key role in the background. How smart the app or the device is depends on the selection of the right machine learning technique that will grant the best interpretation of the data and signals involved to achieve the desired outcome. This is mainly why machine learning techniques are progressing rapidly and attracting the attention of researchers and investors, especially in the medical field.

This Special Issue will focus on utilizing machine learning in biomedical image analysis. Researchers are encouraged to submit original research articles or review articles discussing the state-of-the-art machine learning techniques for medical applications, including, but not limited to, the early diagnosis of various kinds of cancer and neurological disorders. The articles are expected to provide an extensive description of the machine learning technique(s) involved, as well as the medical application,  highlighting the performance evaluation metrics used.

Prof. Dr. Ayman El-Baz
Dr. Jasjit S. Suri
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
  • biomedical image analysis
  • diagnosis
  • cancer
  • neurological disorders

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Related Special Issue

Published Papers (3 papers)

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Research

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16 pages, 4085 KiB  
Article
Content-Based Image Retrieval and Image Classification System for Early Prediction of Bladder Cancer
by Muhammed Yildirim
Diagnostics 2024, 14(23), 2637; https://doi.org/10.3390/diagnostics14232637 - 22 Nov 2024
Abstract
Background/Objectives: Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection [...] Read more.
Background/Objectives: Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection of bladder cancer is, therefore, of great importance. Methods: Therefore, this study developed two systems based on classification and Content-Based Image Retrieval (CBIR). The primary purpose of CBIR systems is to compare the visual similarities of a user-provided image with the images in the database and return the most similar ones. CBIR systems offer an effective search and retrieval mechanism by directly using the content of the image data. Results: In the proposed CBIR system, five different CNNs, two different textural-based feature extraction methods, and seven different similarity measurement metrics were tested for feature selection and similarity measurement. Successful feature extraction methods and similarity measurement metrics formed the infrastructure of the developed system. Densenet201 was preferred for feature extraction in the developed system. The cosine metric was used in the proposed CBIR system as a similarity measurement metric, the most successful among seven different metrics. Conclusions: As a result, it was seen that the proposed CBIR model showed the highest success using the Densenet201 model for feature extraction and the Cosine similarity measurement method. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis—2nd Edition)
41 pages, 33915 KiB  
Article
Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans
by Siddharth Gupta, Arun K. Dubey, Rajesh Singh, Mannudeep K. Kalra, Ajith Abraham, Vandana Kumari, John R. Laird, Mustafa Al-Maini, Neha Gupta, Inder Singh, Klaudija Viskovic, Luca Saba and Jasjit S. Suri
Diagnostics 2024, 14(14), 1534; https://doi.org/10.3390/diagnostics14141534 - 16 Jul 2024
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Abstract
Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease [...] Read more.
Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis—2nd Edition)
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Review

Jump to: Research

21 pages, 2489 KiB  
Review
Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review
by Katarzyna Kryszan, Adam Wylęgała, Magdalena Kijonka, Patrycja Potrawa, Mateusz Walasz, Edward Wylęgała and Bogusława Orzechowska-Wylęgała
Diagnostics 2024, 14(7), 694; https://doi.org/10.3390/diagnostics14070694 - 26 Mar 2024
Viewed by 2155
Abstract
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part [...] Read more.
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the “black-box” nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis—2nd Edition)
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