Computer-aided diagnosis (CAD) has made considerable progress in the last decades, resulting in the development of several effective CAD systems. Recent advances in machine learning (ML) have opened up novel avenues for computer-assisted diagnosis of Pleural mesothelioma. Additionally, improvements in ML techniques, the majority of which are based on DL, have substantially impacted the performance of CAD systems.

Currently, the medical sector demands more creative technology to handle vast amounts of data and enhance the quality of service provided to patients. It also requires an intelligent system to identify early symptoms of multiple infections and give suitable treatment. The Internet of Things in Healthcare (HIoT) and its recent breakthroughs have added a new dimension to medical sector operations and the realization of an intelligent system. A significant recent breakthrough via deep learning (DL) techniques has garnered interest in academic research and business application groups. DL is the most rapidly expanding discipline of machine learning, with extensive applications in the prognosis of Pleural Mesothelioma. Recent studies have shown that DL may dramatically improve the diagnosis prediction of contagious diseases. Hence, ML/DL approaches can enhance the accuracy of CAD systems and rethink their design.

This Special Issue intends to highlight emerging subjects and provides a forum for discussing future advancements and novel ideas in the healthcare sector, particularly with the advent of deep learning-based biomedical systems in the HIoT for the prognosis of different Pleural mesothelioma. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Automated learning in Mesothelioma Diagnosis
• Mesothelioma drug discovery
• Internet of Medical Things (IoMT)
• CAD using Biomedical Images
• Mesothelioma prediction using RNA-sequences
• Mesothelioma detection using chest X-rays
• Applications of Computer-Assisted Detection/Diagnosis
• Supervised Learning for Mesothelioma detection
• Computational Expert Systems for Mesothelioma
• Computer-Aided Decision Support Systems
• Data mining and knowledge discovery for Mesothelioma survival
• Clinical Decision support systems
• Neural networks for prognosis of Mesothelioma