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 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