Dear Colleagues,

Medical data contain information on a person's state of health and the medical treatment that they have received such as signals, images, sounds, chemical components and their concentration, body temperature, respiratory rate, blood pressure, and different treatment measurements to quantify the patient’s status and the disease stage. Nowadays, 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 to detect, classify, segment, and measure various structures. Medical experts rely on the 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. Employing technological tools for collection, processing, and analysis will incorporate understanding the patient’s status and developing the treatment plan. Achieving highly accurate models needs huge datasets; this issue can be solved by having enough knowledge of medical data processing and its analysis.

In this Special Issue, “Advances in Medical Image Processing, Segmentation and Classification”, we will cover original articles, short communication, and reviews related to various computer-aided diagnosis methods for biomedical systems. Applications such as patient monitoring, disease diagnosis and progression, patient rehabilitation, and medical image analysis are encouraged. It is expected that you clearly indicate the novel aspects of signal processing or modelling that assisted you in solving your problem.

Dr. Wan Azani Mustafa
Dr. Hiam Alquran
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 2000 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.

• medical image/bio-signal analysis
• medical image segmentation/detection
• healthcare systems
• AI-based medical image registration
• medical image recognition
• biomedical systems
• diagnostic aid
• AI-based screening system
• medical image
• signal classification
• biomedical image retrieval
• medical image annotation
• biomedical image summarization/filtering
• cancer diagnosis
• machine learning
• deep learning
• artificial intelligence
• AI-based medical image diagnosis
• medical deep learning CAD systems
• XAI-based medical imaging
• patient/treatment stratification based on AI image processing
• synthetic medical image generation
• explainable AI in medicine