A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Rehman, Mujeeb Ur; Shafique, Arslan; Ghadi, Yazeed Yasin; Boulila, Wadii; Jan, Sana Ullah; Gadekallu, Thippa Reddy; Driss, Maha; Ahmad, Jawad

Abstract

Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This study proposes a novel privacy-preserving non-invasive cancer detection method using Deep Learning (DL). Initially, the clinical data is collected over the Internet via wireless channels for diagnostic purposes. It is paramount to secure personal clinical data against eavesdropping by unauthorized users that may exploit it for personalized interests. Therefore, the collected data is encrypted before transmission over the channel to prevent data theft. Various security measures, including correlation, entropy, contrast, structural content, and energy, are used to assess the proposed encryption method's efficiency. In this paper, we proposed using the Convolutional Neural Network (CNN)-based model and Magnetic Resonance Imaging (MRI) with different techniques, including transfer learning, fine-tuning, and K-fold analysis cancer detection. Extensive experiments are carried out to evaluate the performance of the proposed DL techniques with regard to traditional machine learning approaches such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results show that the CNN-based model has achieved an accuracy of 98.9% and outperforms conventional ML algorithms. Further experiments demonstrate the efficiency of both encryption schemes, achieving entropy, contrast, and energy of 7.9999, 10.9687, and 0.0151, respectively.

Citation

Rehman, M. U., Shafique, A., Ghadi, Y. Y., Boulila, W., Jan, S. U., Gadekallu, T. R., …Ahmad, J. (2022). A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis. IEEE Transactions on Network Science and Engineering, 9(6), 4322-4337. https://doi.org/10.1109/TNSE.2022.3199235

• Journal Article Type: Article
• Acceptance Date: Aug 7, 2022
• Online Publication Date: Aug 17, 2022
• Publication Date: 2022-12
• Deposit Date: Oct 17, 2022
• Journal: IEEE Transactions on Network Science and Engineering
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 6
• Pages: 4322-4337
• DOI: https://doi.org/10.1109/TNSE.2022.3199235
• Keywords: Encryption, Cancer, Security, Magnetic resonance imaging, Force, Deep learning, Cancer detection
• Public URL: http://researchrepository.napier.ac.uk/Output/2917286