Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is a blood cancer characterized by the rapid proliferation of immature white blood cells, demanding accurate and timely diagnosis for effective treatment. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) like ResNet, VGG, and Inception architectures, to analyze microscopic blood smear images for automated ALL detection and classification, often incorporating techniques like transfer learning and multiple instance learning to improve accuracy and address data limitations. These advancements offer the potential to significantly improve diagnostic speed and accuracy, reducing reliance on time-consuming manual analysis and potentially improving patient outcomes.