A Web-Based Framework for Brain Hemorrhage Detection from CT Scans Using Deep Learning

This paper presents a deep learning-based web framework for automated detection of brain hemorrhage from CT scan images. Brain hemorrhage is a critical medical condition that requires early and accurate diagnosis to reduce mortality and improve patient outcomes. The proposed system utilizes a Convolutional Neural Network (CNN) to classify CT scan images into multiple risk categories, including normal, moderate risk, and high risk. To enhance interpretability and trust in predictions, the system integrates Gradient-weighted Class Activation Mapping (Grad-CAM) to generate heatmaps highlighting affected regions in the brain. The framework is implemented as a web-based platform that allows users to upload CT scan images, receive instant predictions, and generate diagnostic reports. The system also includes modules for patient data management, report generation, and analytics visualization. Experimental results demonstrate an accuracy of 92–95% with low prediction latency, making the system suitable for real-time clinical support. This work contributes to the development of intelligent healthcare systems by combining deep learning, explainable AI, and web technologies to provide an efficient and scalable solution for brain hemorrhage detection.