The traditional and semi-automatic approaches employed in disease diagnosis and treatment caused a large disease diagnostic burden on doctors as well as hospitals. The improvement in the computing facilities helped to considerably overcome the problem. The traditional and multi-specialty hospitals are actively moving toward computer-supported disease screening, assessments, decision making, and treatment implementation procedures due to its advancements and improved accuracy.

During mass disease screening procedures and screening of infected patients, it produces considerably large medical data to be examined using a chosen computerized method and the final evaluation by doctors to detect the disease and execute the appropriate medication to cure the disease. Handling medical big-data is quite complex compared to conventional data. Recently, a considerable number of machine-learning, deep-learning, and hybrid methods have been proposed and implemented to examine the gig-data to detect the disease in its early phase with considerable accuracy.

This Special Issue encourages academic and clinical experts to present their ground-breaking and pioneering research work related to recent methods employed to evaluate traditional and big-data associated with the medical domain. We invite authors to submit novel machine learning schemes for biomedical signal/image assessment, deep-learning supported analysis, and combining the machine and deep-learning scheme to achieve better diagnostic accuracy. We welcome original research and review articles associated with deep learning-based clinical data evaluation.

Potential topics include but are not limited to the following:

• Pre-trained deep-learning scheme for 1D, 2D, and 3D medical data assessment
• Customary deep-learning, machine-learning, and combined algorithms for medical data evaluation
• Deep-learning supported segmentation and classification of medical big-data
• Ensemble deep-learning approaches for medical big-data assessment
• Disease detection, treatment, and recovery monitoring based on medical data assessments
• Disease modeling and model-assisted treatment with medical big-data
• Heuristic algorithm supported pre-processing and post-processing for clinical big-data
• Deep-learning, IoT, and cloud computing for clinical big-data analytics