Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications

Abstract

The integration of deep learning into cloud-based healthcare systems has opened new frontiers in medical image analysis, enabling faster, more accurate, and accessible diagnostics. However, the high computational demands of conventional deep learning models pose significant challenges for deployment in cloud environments, especially in latency-sensitive and resource-limited settings. This study aims to optimize deep learning algorithms to enhance their efficiency and scalability for medical image detection within cloud computing infrastructures. A quantitative research approach was employed, involving algorithmic optimization techniques such as pruning, quantization, transfer learning, and federated learning. The models were tested using benchmark medical image datasets and deployed in a simulated cloud environment to evaluate performance metrics such as accuracy, inference time, resource usage, and privacy compliance. Results showed that optimized models, particularly EfficientNet with pruning and quantization, achieved high diagnostic accuracy (up to 91.7%) while significantly reducing computational overhead. Federated learning proved effective in maintaining data privacy with minimal loss in accuracy. The findings suggest that lightweight, secure, and fast deep learning models can be realistically integrated into cloud-based healthcare applications. This study contributes a framework for efficient and scalable AI deployment in clinical settings, particularly in underserved or remote areas.