Article Per Year (5 Year)
p-Index From 2021 - 2026
1.77
P-Index
This Author published in this journals
All Journal Community Development Journal: Jurnal Pengabdian Masyarakat Abdimas Awang Long: Jurnal Pengabdian dan Pemberdayaan Masyarakat International Journal Software Engineering and Computer Science (IJSECS) Bulletin of Network Engineer and Informatics (BUFNETS) Global Education Journal The Journal of Artificial Intelligence and Development
Putri, Desfita Eka
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : The Journal of Artificial Intelligence and Development

 1 
Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications Putri, Desfita Eka; Prayudani, Santi; Sitopu, Joni Wilson
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Afriani, Gusma Asfahani, Asfahani Ayi Abdurahman Baso Intang Sappaile, Baso Intang Deyidi Mokoginta Fini Widya Fransiska, Fini Widya Ika Wulandari Iwan Adhicandra Krisnawati, Nova Liza Husnita Mardhotillah, Bunga Prayudani, Santi Rena Augia Putrie Sa'diyah, Sa'diyah Wattimena, Fegie Yoanti