A Framework for Privacy-Preserving Detection of Sickle Blood Cells Using Deep Learning and Cryptographic Techniques
Kholoud Alotaibi, Naser El-Bathy · International Journal of Advanced Computer Science and Applications · 2024
Sickle cell anemia is a hereditary disorder where abnormal hemoglobin causes red blood cells to become rigid and crescent-shaped, obstructing blood flow and leading to severe health complications. Early detection of these abnormal cells is essential for timely treatment and reducing disease progression. Traditional screening methods, though effective, are time-intensive and require skilled technicians, making them less suitable for large-scale implementation. This paper presents a conceptual framework that integrates transfer learning, cryptographic algorithms, and service-oriented architecture to provide a secure and efficient solution for sickle cell detection. The framework uses MobileNet, a lightweight deep learning model, enhanced with transfer learning to identify sickle cells from medical images while operating on hardware-constrained environments. Advanced Encryption Standards (AES) ensure sensitive patient data remains secure during transmission and storage, while a service-oriented architecture facilitates seamless interaction between system components. Although not yet implemented, the framework serves as a foundation for future empirical testing, addressing the need for accurate detection, data privacy, and system efficiency in healthcare applications.