Due to the rapid development of algorithms, hardware, and the huge increase in the volume of data, deep learning (DL) algorithms have been widely employed to address complex problems in a variety of fields, including medical applications such as medical image processing or medical data mining. Most recently, neuro-evolution and metaheuristic optimization algorithms have been used to solve more complex problems, as well as to optimize DL models. Different metaheuristic optimization algorithms have been inspired by the behaviors of swarms, birds, and animals. Hybrid metaheuristic algorithms have also been adopted as an advanced solution to more complex problems. Hybridization approaches have been extended to merge traditional machine learning methods or advanced deep learning methods with metaheuristic algorithms due to the ability of the metaheuristic techniques to find optimal solutions. Therefore, this gives these approaches great potential for medical applications, such as image segmentation, elderly monitoring, and text analysis and classification.

Medical data increases daily, collected from different systems, such as hospitals, health organizations, wearable devices (for example, to track the activities of the elderly), smartphone sensors, smart homes, and air quality records, among others. Therefore, it is necessary to develop more robust systems to deal with the high dimensions of data using optimized DL schemes. Traditional methods face critical challenges in dealing with different medical data, including images, text, and others. The main challenges of these data are the high dimensionality and large size that require more time, and so hybrid deep learning and neuro-evolution metaheuristic optimization algorithms could provide more efficient solutions.

The main goal of this Special Issue is to gather the latest research in advanced deep learning and neuro-evolution metaheuristic optimization algorithms for medical applications. We welcome both original research and review articles

Potential topics include but are not limited to:

Potential topics include but are not limited to the following:

• - Hybrid deep learning and metaheuristic algorithms for medical applications
• - Public health big data mining and processing
• - Air quality index time series analysis and forecasting
• - Elderly health monitoring using collected medical data from wearable sensors
• - Infectious disease spread time series analysis and prediction
• - CT image processing, segmentation, and classification
• - Medical text analysis and classification
• - Brain cancer MRI image processing, segmentation, and classification
• - Medical data analysis and management
• - Swarm intelligence applications for medical data
• - Patient monitoring in Internet of Things (IoT) environments
• - Biosensor applications for healthcare.
• - Internet of Medical Things (IoMT) applications for healthcare