Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—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: 31 March 2025 | Viewed by 436
Special Issue Editor
Interests: image classification; image segmentation; medical image processing; biomedical optical imaging; medical signal processing; artificial intelligence; deep learning; machine learning; computer-aided diagnosis; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
A computer-aided diagnosis (CAD) system involves various stages such as detection, segmentation and classification. Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable and robust, which helps physicians detect, classify, segment and measure various structures. Medical experts rely on medical imaging modalities such as computed tomography (CT), microscopic blood smear images, magnetic resonance imaging (MRI), X-ray and ultrasound (US) to diagnose health challenges and assign treatment prescriptions. Researchers and developers are able to deliver smart solutions for medical imaging diagnoses thanks to the AI-based potential functionalities of machine learning and deep learning technologies.
In this Special Issue, we will cover original articles, short communication and reviews related to various deep learning techniques and computer-aided diagnoses for biomedical systems. We invite all potential authors to submit their research contributions to explore the possible methodologies and techniques for the healthcare environment.
This Special Issue is dedicated to high-quality, original research papers in the overlapping fields of the following:
AI-based medical image diagnosis;
Medical deep learning CAD systems;
XAI-based medical imaging;
Medical image/bio-signal analysis;
Medical image segmentation;
Medical image segmentation;
Hybrid medical knowledge generation;
Deep reinforcement learning;
Healthcare systems;
AI-based prognosis and recommendations.
Dr. Mugahed A. Al-antari
Guest Editor
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
- image classification
- image segmentation
- medical image processing
- biomedical optical imaging
- medical signal processing
- artificial intelligence
- deep learning
- machine learning
- computer-aided diagnosis
- explainable artificial intelligence
