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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

A discrete mathematical model and cryptography for secure medical image analysis : Encrypted chest X-ray classification

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* Corresponding author · click or hover a name for details

pp. 1473–1486Vol. 28Issue 5-AAugust 2025DOI: 10.47974/JDMSC-2146 Crossmark XML
Received:
05 Nov 2024
Published Online:
30 Aug 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2146
Pages:
1473–1486

Abstract

This research presents a privacy-preserving approach to chest X-ray image classification utilizing deep learning techniques integrated with Advanced Encryption Standard (AES) encryption and Fully Homomorphic Encryption (FHE) to maintain data confidentiality. The methodology employs various Convolutional Neural Network (CNN) models to classify chest X-rays into three categories: normal, COVID-19 infected, and pneumonia infected, without decrypting the data. The process involves preprocessing the images, determining optimal hyperparameters, and fine-tuning the CNN models through transfer learning. The models evaluated include ResNet50, ResNet101, MobileNetV3 Small, MobileNetV3 Large, DenseNet121, and DenseNet161, with performance metrics such as accuracy, F1-score, precision, recall (sensitivity), and specificity used for comprehensive assessment. Among the tested models, DenseNet161 achieved the highest accuracy of 92.28%, followed by DenseNet121 and ResNet50 with accuracies of 91.41% and 91.26%, respectively. This study demonstrates the feasibility and effectiveness of combining encryption with deep learning for secure and accurate medical image analysis, thereby addressing the critical need for privacy in medical diagnostics.

Keywords

Advanced encryption standardFully homomorphic encryptionMedical image classificationPrivacy-preserving techniquesChest x-ray analysisTransfer learning

Subject Classifications

68T0768P25

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