Publication

Abstract : Advancements in medical imaging has greatly improved the diagnosis and treatment of neurological disorders. Prompt identification of brain hemorrhage is essential for enhancing patient outcomes. This research introduces an innovative deep learning approach to automate the detection of brain hemorrhage in CT scans. It involves thorough data preprocessing, including synthetic data generation, and utilizes advanced convolutional neural networks (CNNs) with recurrent layers and ResNet models. The models are trained with dynamic augmentation techniques using an image generator to improve generalization. Evaluation methods include traditional metrics and a web application that loads a pre-trained model, providing heatmaps of Region of Interest (ROI) indicating hemorrhage or normal conditions. This tool aids in interpreting the model’s predictions, potentially improving the accuracy of brain hemorrhage detection in medical practice. By integrating deep learning with visualization techniques, this methodology aims to streamline diagnosis, enabling radiologists to make more informed decisions efficiently. Ultimately, it could lead to better patient care through early detection and intervention for brain hemorrhages.