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Diagnostics
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Lesion Detection and Analysis Using Artificial Intelligence, Third Edition
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Lesion Detection and Analysis Using Artificial Intelligence, Third 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: 30 June 2025 | Viewed by 1023

Special Issue Editors

Prof. Dr. Luca Saba

E-Mail Website
Guest Editor
Department of Radiology, University of Cagliari, 09042 Cagliari, Italy
Interests: neuroradiology; vascular imaging; cardiovascular imaging
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,

Artificial intelligence (AI), including deep learning and machine learning, is currently undergoing rapid development, having garnered substantial public attention in recent years. This Special Issue plans to focus on topics and issues regarding the development AI to become more meaningfully intelligent for lesion detection and analysis, scientific validations of AI systems, clinical evaluations of AI systems, bias detection in AI systems, high-speed AI systems, and edge-devices for AI systems, with all these facets of AI enveloping different branches of medicine and leading to personalized and precision medicine.

Prof. Dr. Luca Saba
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
  • deep learning
  • machine learning
  • lesion detection and analysis
  • diagnosis

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Published Papers (2 papers)

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Research

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28 pages, 8102 KiB  
Article
Multiclassification of Colorectal Polyps from Colonoscopy Images Using AI for Early Diagnosis
by Jothiraj Selvaraj, Kishwar Sadaf, Shabnam Mohamed Aslam and Snekhalatha Umapathy
Diagnostics 2025, 15(10), 1285; https://doi.org/10.3390/diagnostics15101285 - 20 May 2025
Abstract
Background/Objectives: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the critical need for the accurate classification of precancerous polyps. This research presents an extensive analysis of the multiclassification framework leveraging various deep learning (DL) architectures for the [...] Read more.
Background/Objectives: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the critical need for the accurate classification of precancerous polyps. This research presents an extensive analysis of the multiclassification framework leveraging various deep learning (DL) architectures for the automated classification of colorectal polyps from colonoscopy images. Methods: The proposed methodology integrates real-time data for training and utilizes a publicly available dataset for testing, ensuring generalizability. The real-time images were cautiously annotated and verified by a panel of experts, including post-graduate medical doctors and gastroenterology specialists. The DL models were designed to categorize the preprocessed colonoscopy images into four clinically significant classes: hyperplastic, serrated, adenoma, and normal. A suite of state-of-the-art models, including VGG16, VGG19, ResNet50, DenseNet121, EfficientNetV2, InceptionNetV3, Vision Transformer (ViT), and the custom-developed CRP-ViT, were trained and rigorously evaluated for this task. Results: Notably, the CRP-ViT model exhibited superior capability in capturing intricate features, achieving an impressive accuracy of 97.28% during training and 96.02% during validation with real-time images. Furthermore, the model demonstrated remarkable performance during testing on the public dataset, attaining an accuracy of 95.69%. To facilitate real-time interaction and clinical applicability, a user-friendly interface was developed using Gradio, allowing healthcare professionals to upload colonoscopy images and receive instant classification results. Conclusions: The CRP-ViT model effectively predicts and categorizes colonoscopy images into clinically relevant classes, aiding gastroenterologists in decision-making. This study highlights the potential of integrating AI-driven models into routine clinical practice to improve colorectal cancer screening outcomes and reduce diagnostic variability. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence, Third Edition)

Review

Jump to: Research

32 pages, 3872 KiB  
Review
Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review
by Vandana Kumari, Alok Katiyar, Mrinalini Bhagawati, Mahesh Maindarkar, Siddharth Gupta, Sudip Paul, Tisha Chhabra, Alberto Boi, Ekta Tiwari, Vijay Rathore, Inder M. Singh, Mustafa Al-Maini, Vinod Anand, Luca Saba and Jasjit S. Suri
Diagnostics 2025, 15(7), 848; https://doi.org/10.3390/diagnostics15070848 - 26 Mar 2025
Viewed by 699
Abstract
Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into [...] Read more.
Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence, Third Edition)
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