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Privacy-Aware AI-based Models for Cancer Diagnosis

Submission Deadline: 30 July 2024 View: 308 Submit to Special Issue

Guest Editors

Dr. Jawad Ahmad, Edinburgh Napier University, UK
Dr. Syed Aziz Shah, Coventry University, UK
Dr. Wadii Boulila, Prince Sultan University, Saudi Arabia


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. 


Cancer Detection; Healthcare Systems; Big Data Analytics; AI; Deep Learning; Machine Learning; Edge Intelligence; Security; Privacy; Internet of Things

Published Papers

  • Open Access


    Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

    Muhammad Armghan Latif, Zohaib Mushtaq, Saad Arif, Sara Rehman, Muhammad Farrukh Qureshi, Nagwan Abdel Samee, Maali Alabdulhafith, Yeong Hyeon Gu, Mohammed A. Al-masni
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621
    (This article belongs to the Special Issue: Privacy-Aware AI-based Models for Cancer Diagnosis)
    Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of… More >

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