Abstract: This project integrates cutting-edge computer vision techniques used to enhance the medical diagnostics by simultaneously addressing two critical aspects of healthcare—blood sample analysis for leukemia and brain hemorrhage classification in MRI images. Leveraging the YOLO (You Only Look Once) algorithm, our system employs deep learning to efficiently detect and classify various stages of leukemia in blood samples. YOLO's real- time object detection capabilities enable swift identification of abnormal cells, facilitating early diagnosis and intervention. The model is trained on a comprehensive dataset, ensuring robust performance across diverse cases. In parallel, Convolutional Neural Networks (CNNs) are employed for the intricate task of brain hemorrhage classification in MRI scans. The CNN model learns complex hierarchical features from brain images, enabling it to accurately differentiate between different types and stages of hemorrhages. This dual-faceted approach aims to provide a comprehensive diagnostic tool, facilitating healthcare professionals in timely and accurate decision-making.
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DOI:
10.17148/IJARCCE.2025.14389
