Blood Cancer Cell Detection Using YOLO with 3D Depth Imaging for Automated Hematological Diagnosis

Early detection of blood cancers remains a critical challenge due to the limitations of conventional manual blood smear examinations, which are slow and dependent on expert interpretation. In response, this study presents an AI-driven approach that leverages the YOLO object detection framework paired with a standard webcam for real-time analysis of blood samples. Trained on a dataset of major blood cell types, the system demonstrates promising capability in distinguishing abnormal cells. By offering a cost-effective and accessible diagnostic aid, the method shows potential to support pathologists in the early screening of leukemia, lymphoma, myeloma, and related disorders.