IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 401 10.51583/IJLTEMAS.2026.150900050 Biomedical Engineering Artificial Intelligence (AI) in Medical Image-Pneumonia and Bone Fracture Detection Tojibul Islam Shaikh Biomedical Engineering, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh Bangladesh Dewan Bivas Biomedical Engineering, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh Bangladesh Miraj Mohammad Biomedical Engineering, University of Malaya, Kuala Lumpur, Malaysia. Malaysia Al-Mamun Md. Biomedical Engineering, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh Bangladesh Hakim Swarnaly Biomedical Engineering, Bangladesh University of Health Sciences (BUHS), Dhaka, Bangladesh Bangladesh 15 9 656 673 13 09 2026 18 09 2026 07 10 2026 Artificial Intelligence predictions Medical Image Convolutional Neural Network Django PostgreSQL HTML5 CSS3 Bootstrap Javascript JSON API Abstract

Medical X-ray image interpretation requires considerable clinical expertise and may become challenging when the workload is high or access to experienced medical professionals is limited. This study presents a web-based medical image analysis application developed using the Django framework for the automated classification of pneumonia from chest X-ray images and bone fractures from skeletal X-ray images. The proposed system integrates deep learning-based image classification with a web application interface, allowing users to upload X-ray images and obtain model-generated predictions through a single platform. The application also incorporates image validation, prediction-result storage, and an AI-assisted explanation module using the Google Gemini API to present the prediction in a more understandable form. The system was developed to demonstrate the practical integration of deep learning models into an accessible medical imaging workflow rather than to replace professional clinical diagnosis. The application provides a structured workflow from image submission and preprocessing to model inference and presentation of the predicted result. The study also discusses the limitations associated with image quality, dataset diversity, classification coverage, and the absence of explicit lesion or fracture localization. The proposed system demonstrates how a web-based deep learning application can be used as a supportive tool for preliminary medical image assessment and as a platform for further research in AI-assisted medical imaging.

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