Special Issue "Artificial Intelligence in Medical Diagnosis and Lesion Classification"

Medical images contain a lot of information about a patient's health state and guide specialists to the appropriate medical treatment or further health procedure. The treatment comes from the visualization of the internal organs besides their functionality. Moreover, it provides information about the presence or absence of lesions in various organs such as the liver, lung, kidney, etc.  Nowadays, a computer-aided diagnosis (CAD) system involves various phases such as detection, segmentation, and classification. Over the last few decades, computer-aided diagnosis and detection systems have become a part of clinical practice, and they facilitate the procedure of making decisions. The availability of such systems assists 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. Physicians 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, and follow up the progress of the desired organ. Researchers and developers can provide intelligent solutions for medical imaging diagnostics. Employing technological tools for collection, processing, and analysis will incorporate understanding the patient’s status and developing the treatment plan. Obtaining a highly accurate model needs large datasets besides having enough knowledge of image processing. This issue can be solved by gaining sufficient experience in medical data processing and its analysis. 

In this Special Issue, “Artificial intelligence in medical diagnosis and lesion classification”, we will cover original articles, short communication, and reviews related to various computer-aided diagnoses for biomedical systems. Applications such as patient monitoring, disease diagnosis and progression, patient rehabilitation, and medical image analysis are encouraged. We anticipate obtaining novel aspects of signal processing or modeling that facilitate solving problems.

Dr. Wan Azani Mustafa
Dr. Hiam Alquran
Guest Editors

Manuscript Submission Information

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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 and 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