Journal of Intelligent Systems and Internet of Things
JISIoT
2690-6791
2769-786X
10.54216/JISIoT
https://www.americaspg.com/journals/show/3009
2019
2019
Deep Learning-Based model for Medical Image Compression
Department of Software College of Information Technology University of Babylon, Babylon, Iraq
Saad
Saad
Department of Software College of Information Technology University of Babylon, Babylon, Iraq
Tawfiq A. AL
AL-Assadi
Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an analysis of a brain MRI dataset. It maintains competitive compression ratios while producing higher (PSNR) and (MSE), indicating higher picture integrity and low information loss. To strike a good balance between the critical need for accurate diagnosis and the economical use of resources, this study offers a possible method for compressing medical images.
2024
2024
192
201
10.54216/JISIoT.130215
https://www.americaspg.com/articleinfo/18/show/3009