Acute Lymphoblastic Leukemia Subtypes Detection using Vision Transformer Model

Acute Lymphoblastic Leukemia (ALL) is a fast progressing cancer that affects white blood cells and requires precise and timely diagnosis for treatment. Current methods of diagnosis are manual and based on histopathological images and are prone to variability and inefficiencies. Recent advancements in deep learning especially transformer based architectures have shown significant improvements in medical image analysis. This paper explores the use of Vision Transformers (ViT) for automatic detection of ALL from microscopic blood smear images. The Vision Transformer model achieved an accuracy of 98.01%, with precision, recall, and F1-score all consistently at 98.00%, showing good overall performance.