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

(Received: 6-Aug.-2024, Revised: 1-Oct.-2024 , Accepted: 24-Oct.-2024)

Authors Ghazala Hcini, Imen Jdey,

Keywords #Deep learning #U-net architecture #Spatial-attention mechanism #K-anonymity #Privacy preservation #Cross-domain transfer

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%, a precision of 99.42%, a 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.

References

[1] E. O. Kolawole et al., "Malaria Endemicity in Sub-Saharan Africa: Past and Present Issues in Public Health," Microbes and Infectious Diseases, vol. 4, no. 1, pp. 242-251, 2023.

[2] J. Li et al., "Current Status of Malaria Control and Elimination in Africa: Epidemiology, Diagnosis, Treatment, Progress and Challenges," Journal of Epidemiology and Global Health, vol. 14, no. 3, pp. 561-579, DOI: 10.1007/s44197-024-00228-2, 2024.

[3] P. Venkatesan, "The 2023 WHO World Malaria Report," The Lancet Microbe, vol. 5, no. 3, p. e214, 2024.

[4] A. Mbanefo and N. Kumar, "Evaluation of Malaria Diagnostic Methods As a Key for Successful Control and Elimination Programs," Tropical Medicine and Infectious Disease, vol. 5, no. 2, p. 102, 2020.

[5] P. Gupta, "Rapid Diagnostic Tests for Malaria: Challenges and Future Prospects, a Brief Review," Challenges and Advances in Pharmaceutical Research, vol. 8, pp. 152-62, 2022.

[6] O. O. Oyegoke et al., "Malaria Diagnostic Methods with the Elimination Goal in View," Parasitology Research, vol. 121, no. 7, pp. 1867-1885, 2022.

[7] S. Minaee et al., "Image Segmentation Using Deep Learning: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2021.

[8] M. Z. Khan et al., "Deep Neural Architectures for Medical Image Semantic Segmentation," IEEE Access, vol. 9, pp. 83002-83024, DOI: 10.1109/ACCESS.2021.3086530, 2021.

[9] D. D. Patil and S. G. Deore, "Medical Image Segmentation: A Review," International Journal of Computer Science and Mobile Computing, vol. 2, no. 1, pp. 22-27, 2013.

[10] I. Jdey et al., "Fuzzy Fusion System for Radar Target Recognition," International Journal of Computer Applications & Information Technology, vol. 1, no. 3, pp. 136-142, 2012.

[11] G. Hcini et al., "HSV-Net: A Custom CNN for Malaria Detection with Enhanced Color Representation," Proc. of the 22th IEEE Int. Conf. on Cyberworlds (CW), pp. 337-340, Sousse, Tunisia, 2023.

[12] G. H Hcini, I. Jdey and H. Ltifi, "Improving Malaria Detection Using L1 Regularization Neural Network," JUCS: Journal of Universal Computer Science, vol. 28, no. 10, pp. 1087-1107, 2022.

[13] M. A. Abdou, "Literature Review: Efficient Deep Neural Networks Techniques for Medical Image Analysis," Neural Computing and Applications, vol. 34, no. 8, pp. 5791-5812, 2022.

[14] Tobias Mourier et al., "The Genome of the Zoonotic Malaria Parasite Plasmodium Simium Reveals Adaptations to Host Switching," BMC Biology, vol. 19, p. 219, pp. 1-17, 2021.

[15] D. Sukumarran et al., "Machine and Deep Learning Methods in Identifying Malaria through Microscopic Blood Smear: A Systematic Review," Engineering Applications of Artificial Intelligence, vol. 133, no. E, p. 108529, 2024.

[16] Y. Lv et al., "Attention Guided U-Net with Atrous Convolution for Accurate Retinal Vessels Segmentation," IEEE Access, no. 8, pp. 32826-32839, DOI: 10.1109/ACCESS.2020.2974027, 2020.

[17] G. Du et al., "Medical Image Segmentation Based on U-Net: A Review," Journal of Imaging Science & Technology, vol. 64, Article ID: jist0710, 2020.

[18] R. Azad et al., "Medical Image Segmentation Review: The Success of U-Net," IEEE Trans. on Pattern Analysis and Machine Intelligence, Early Access, pp. 1-20, DOI: 10.1109/TPAMI.2024.3435571, 2024.

[19] A. Ben Hamida et al., "Deep Learning for Colon Cancer Histopathological Images Analysis," Computers in Biology and Medicine, vol. 136, p. 104730, 2021.

[20] Z. Cheng, A. Qu and X. He, "Contour-aware Semantic Segmentation Network with Spatial Attention Mechanism for Medical Image," The Visual Computer, vol. 38, no. 3, pp. 749-762, 2022.

[21] Z. Chen et al., "An Object Detection Network Based on YOLOv4 and Improved Spatial Attention Mechanism," Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2359-2368, 2022.

[22] V. D. Karalis, "The Integration of Artificial Intelligence into Clinical Practice," Applied Biosciences, vol. 3, no. 1, pp. 14-44, 2024.

[23] I. Jdey, "Trusted Smart Irrigation System Based on Fuzzy IoT and Blockchain," Proc. of the Int. Conf. on Service-oriented Computing (ICSOC 2022), pp. 154–165, Sevilla, Spain, 2022.

[24] C. N. Sowmyarani et al., "Enhanced k-Anonymity Model Based on Clustering to Overcome Temporal Attack in Privacy Preserving Data Publishing," Proc. of the 2022 IEEE Int. Conf. on Electronics, Computing and Comm. Techn. (CONECCT), DOI: 10.1109/CONECCT55679.2022.9865682, Bangalore, India, 2022.

[25] A. Majeed and S. Lee, "Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey," IEEE Access, vol. 9, pp. 8512-8545, 2020.

[26] K. El Emam and F. K. Dankar, "Protecting Privacy Using K-Anonymity," Journal of the American Medical Informatics Association, vol. 15, no. 5, pp. 627-637, 2008.

[27] N. Rismayanti, "Segmentation and Feature Extraction for Malaria Detection in Blood Smears," International Journal of Artificial Intelligence in Medical Issues, vol. 2, no. 1, pp. 18-29, 2024.

[28] S. Aanjan Kumar et al., "Application of Hybrid Capsule Network Model for Malaria Parasite Detection on Microscopic Blood Smear Images," Multimedia Tools and Applications, vol. 2024, DOI: 10.1007/s11042-024-19062-6, 2024.

[29] J. B. Abraham, "Malaria Parasite Segmentation Using U-Net: Comparative Study of Loss Functions," Communications in Science and Technology, vol. 4, no. 2, pp. 57-62, 2019.

[30] F. Tehreem and M. Shahid Farid, "Automatic Detection of Plasmodium Parasites from Microscopic Blood Images," Journal of Parasitic Diseases, vol. 44, no. 1, pp. 69-78, 2020.

[31] A. H. Alharbi et al., "Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models," Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 3922763, 2022.

[32] C. R Maturana et al., "iMAGING: A Novel Automated System for Malaria Diagnosis by Using Artificial Intelligence Tools and a Universal Low-cost Robotized Microscope," Frontiers in Microbiology, vol. 14, p. 1240936, DOI: 10.3389/fmicb.2023.1240936, 2023.

[33] N. Wojtas et al., "Malaria Detection Using Custom Semantic Segmentation Neural Network Architecture," Medycyna Weterynaryjna, vol. 79, no. 8, pp. 406-412, 2023.

[34] Y. M. Kassim et al., "Clustering-based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 5, pp. 1735-1746, 2020.

[35] A. Maqsood et al., "Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images," Applied Sciences, vol. 11, no. 5, p. 2284, 2021.

[36] D. Sukumarran et al., "An Optimised YOLOv4 Deep Learning Model for Efficient Malarial Cell Detection in Thin Blood Smear Images," Parasites & Vectors, vol. 17, no. 1, p. 188, 2024.

[37] H. A. H. Chaudhry et al., "A Lightweight Deep Learning Architecture for Malaria Parasite-type Classification and Life Cycle Stage Detection," Neural Computing and Applications, vol. 36, pp. 19795-19805, 2024.

[38] G. Hcini et al., "Investigating Deep Learning for Early Detection and Decision-making in Alzheimer’s Disease: A Comprehensive Review," Neural Processing Letters, vol. 56, Article no. 153, 2024.

[39] J. Wang et al., "Generalizing to Unseen Domains: A Survey on Domain Generalization," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 8, pp. 8052-8072, 2022.