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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


Mujeeb Ur Rehman

Arslan Shafique

Yazeed Yasin Ghadi

Wadii Boulila

Thippa Reddy Gadekallu

Maha Driss


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.

Journal Article Type Article
Acceptance Date Aug 7, 2022
Online Publication Date Aug 17, 2022
Publication Date 2022-11
Deposit Date Oct 17, 2022
Journal IEEE Transactions on Network Science and Engineering
Electronic ISSN 2327-4697
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 9
Issue 6
Pages 4322-4337
Keywords Encryption, Data security, Security, Magnetic resonance imaging, Convolutional neural networks, Deep learning, Cancer detection
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