The Advanced Role of Artificial Intelligence in Personalized Disease Classification and Evaluation

Dear Colleagues,

Among the most extensively studied research areas are artificial intelligence (AI) and its applications. All forms of medical imaging have undergone a revolution in recent years thanks to AI, including dermoscopic images, X-rays, computed tomography, magnetic resonance imaging, positron imaging tomography, fundus imaging, ultrasound imaging, etc. Several AI models have been designed to analyze medical images and perform speedy and accurate disease detection.

A growing number of features in contemporary medical imaging are produced by various types of analysis, including AI. These features are utilized for the analysis of, for example, fuzzy sets, machine learning, deep learning, generative adversarial networks, optimization algorithms, rough sets, uncertainty analysis, long short-term memory, and semantic segmentation. Medical teams can use the findings of these analyses as a guide when evaluating patients. The creation of tools for personalized disease assessment using AI models that take advantage of their ability to recognize patterns and relationships in medical images while utilizing vast quantities of medical images is another challenge faced by AI-driven solutions.

With the aforementioned strategies in mind, the objective of this Special Issue is to deliver the most recent AI research in the domain of medical image processing and analysis. Submissions about novel AI models for medical image analysis are welcome. To address the variations in image spatial–temporal resolution as well as the diversity of biophysical–biochemical mechanisms, contributions addressing other aspects of medical image processing (including, but not limited to, image enhancement, reconstruction/ restoration/registration of images, segmentation, feature extraction/selection and classification are also welcomed. We invite researchers to submit original research articles as well as review articles that discuss the difficulties encountered by artificial intelligence techniques in the analysis of medical images.

Potential topics include, but are not limited to, the following:

• Segmentation and classification of skin cancer A comprehensive review;
• Alzheimer's disease classification using convolutional neural networks;
• Quantum machine learning for anomaly classification using video surveillance;
•  Diabetic retinopathy classification using adversarial neural network;
• Stomach ulcer segmentation and classification using convolutional neural networks;
• Monkeypox disease classification using vision transformers;
• Multi-classification of skin diseases using convolutional neural networks;
• Deep learning models for medical image reconstruction, restoration, registration, segmentation, classification, visualization, and prediction.

Dr. Javeria Amin
Dr. Muhammad Almas Anjum
Guest Editors

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