International Journal of Scientific Research and Engineering Development

The medical imaging has been improving at a rapid pace. generated a tremendous need to have automated diagnostic tools. that support clinicians in stressful surroundings. This paper launches the Medical Image Analyzer, a strong AI-based solution. tion of anomalies and the segmentation thereof with automated detection and segmentation. in X-ray and MRI images. With the help of hybrid architecture of ResNet-50 to have high accuracy and U-Net to be more precise. the system achieves an average accuracy of pixel-level segmentation. 94.2%. The model is installed through a deployment to provide clinical portability. Python flask backend with Kotlin Android interface. Results of experiments illustrate that the system effectively, it offers a reliable second-opinion to radiologists. minimizing diagnostic workload and shortening work with patient time.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"Medical Image Analyzer: A Hybrid Deep Learning Approach for Automated Diagnostic Support on Edge Devices" International Journal of Scientific Research and Engineering Development, V9(2): Page(2560-2562) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.