Guest Editors
Dr. Jawad Ahmad, Edinburgh Napier University, UK
Dr. Syed Aziz Shah, Coventry University, UK
Dr. Wadii Boulila, Prince Sultan University, Saudi Arabia
Summary
Early cancer detection is considered one of the most challenging problems in the field of cancer control. Machine Learning (ML) and Deep Learning (DL) can be utilized as innovative approaches in AI-based healthcare systems for diagnosing cancers. Additionally, as the demand for sharing healthcare data increases, ensuring the privacy of the data becomes more difficult. Initially, clinical data is collected over the Internet using Wi-Fi channels to facilitate doctors in making diagnoses. It is of utmost importance to safeguard personal health data from unauthorized users who may exploit it for their own purposes. To prevent data theft, the collected data should be encrypted before transmission over the channel. Several security measures, such as correlation, entropy, contrast, structural content, and energy, can be employed to assess the effectiveness of the proposed privacy-preserved AI-based cancer diagnosis healthcare system. In this regard, we encourage academics to submit original research articles as well as review articles that aim to explore novel solutions for privacy-preserving AI-based cancer diagnosis models. This special issue will also collect papers in the areas of AI-based smart healthcare models, Blockchain in healthcare, AI-based healthcare cybersecurity systems, edge intelligence for empowering IoT-based healthcare systems, feasibility of ChatGPT in healthcare, cybersecurity concerns in healthcare systems.
Keywords
Cancer Detection; Healthcare Systems; Big Data Analytics; AI; Deep Learning; Machine Learning; Edge Intelligence; Security; Privacy; Internet of Things
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue