PRIVACY-AWARE MALARIA DETECTION: U-NET MODEL WITH K-ANONYMITY FOR CONFIDENTIAL IMAGE ANALYSIS

Abstract

Malaria detection through cell image analysis is essential for early diagnosis and effective treatment, as timely detection can significantly reduce the risk of severe health complications. However, this process raises substantial privacy concerns due to the sensitivity of medical data. This study presents a U-Net model combined with k-anonymity to enhance data security while maintaining high accuracy. The model features a custom Spatial Attention mechanism for improved segmentation performance and incorporates advanced techniques to focus on critical image features. K-Anonymity adds controlled noise to protect data privacy by obfuscating sensitive information. The model achieved a validation accuracy of 99.60%, a Dice score of 99.61%, precision of 99.42%, recall of 99.96%, and an F1 score of 99.69% on malaria cell images. When applied to the Cactus dataset, a real dataset, in agriculture, it achieved an accuracy of 98.58%, an F1 Score of 98.44%, a Dice Score of 95.08%, a Precision of 98.04%, and a Recall of 98.86%, demonstrating its strong generalization capability. These results highlight the effectiveness of integrating privacy-preserving techniques with advanced neural network architectures, improving both security and performance in diverse image analysis applications. [JJCIT 2025; 11(1.000): 85-99]

Keywords

• deep learning
• u-net architecture
• spatial attention mechanism
• k-anonymity
• privacy preservation
• cross-domain transfer