Abstract: Efficient secured storage and retrieval of medical surgical videos are essential for modern healthcare systems. Traditional methods often struggle with scalability, accessibility, and data security, necessitating innovative solutions. This study introduces a novel deep learning-based framework that leverages a hybrid algorithm combining a Variational Autoencoder (VAE) and Group Lasso for optimized video feature selection. This approach reduces dimensionality and enhances the retrieval accuracy of video frames. For storage and retrieval, the system employs a weighted graph-based prefetching algorithm to manage encrypted video data on the cloud, ensuring both speed and security. To ensure data security, video frames are encrypted before cloud storage. Experimental results show that this system outperforms current methods in retrieval speed and accuracy of 99% while maintaining data security. This framework is a significant advancement in medical data management, offering potential applications across other fields that require secure handling of large data volumes.

Megala G and Swarnalatha P, “Optimized Retrieval and Secured Cloud Storage for Medical Surgery Videos Using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01508115

@article{G2024,
title = {Optimized Retrieval and Secured Cloud Storage for Medical Surgery Videos Using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01508115},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01508115},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Megala G and Swarnalatha P}
}