Blast Cell Segmentation in Leukemia Blood Smear Images Using U-Net

Leukemia is a life-threatening blood disorder marked by an abnormal increase in white blood cells, known as blast cells, in the bone marrow. Early detection of these blast cells is critical for effective treatment. Traditionally, diagnosis relies on manual examination of blood smear images by pathologists, a process that is not only time-consuming but also prone to human error. With the rise of ML along with the integration of deep learning , there is a growing opportunity to automate and enhance the diagnostic process. This study explores the application of the U-Net architecture, a neural network model engineered for image segmentation, to automatically detect and segment blast cells in leukemia diagnosis. By automating this process, the aim is to reduce diagnostic time, minimize errors, and improve the overall accuracy of leukemia detection.