Medical imaging plays an important role in diagnose various type of tumours. Patients with symptoms of tumour goes under ultrasound, X-radiation (X-Ray), computer tomography, positron emission tomography (PET) and magnetic resonance imaging (MRI) scanning. The data generated from these methods of imaging is very useful for analysis and decision making.

However, new innovative methods for computer assisted diagnosis (CAD) with other imaging techniques needed for better accuracy and results. In last few decades, emerging techniques such as machine learning, Internet of things (IoT) and Artificial intelligence (AI) playing an important role to locate various types of tumours in human body. The tumour features can be effectively extracted using these techniques which are very difficult to see by eye. The tech savvy methods completely changed the way to make decisions on different life-threatening diseases. The availability of massive amount of medical image data and computational infrastructure, machine learning and deep learning are used to classify and localization of image modalities. Medical data analysis comes with different challenges including lack of sophisticated datasets, high dimensional samples, class imbalance and many more. To deal with such problems, innovative techniques need to design to handle large imaging data efficiently. Meanwhile, Internet of Medical Things (IoMT) techniques also provide better remote diagnosis, real time decision making, prevention and affordable healthcare systems.

The aim of this Special Issue is to discuss the advanced techniques which can help in the identification and cure of various tumours. The advanced medical diagnosis methods in the field of clinical and digital imaging are invited whose role is to provide a global perspective for the identification of various cancerous and non-cancerous human cells. We hope that the readers will gain invaluable insights from the research published in this Special Issue.

Potential topics include but are not limited to the following:

• Application of image processing in healthcare
• Internet of Medical Things (IoMT) application, architecture, security, and technology
• Computer assisted methods
• Evaluation from response to tumour therapy
• Biomedical medical and signal processing
• The role of the Internet of Things (IoT) in tumour therapy
• Multi-modality fusion for analysis and diagnostics
• Advanced computational and learning approaches for diagnostic imaging
• Cancerous and non-cancerous techniques with deep learning approaches
• Tumour therapy-based applications with artificial intelligence