Implementation of Transformer-Based Model for Acute Lymphoblastic Leukemia Segmentation

The examination of peripheral blood smear images for acute lymphoblastic cells represents a diagnostic approach for leukemia. The utilization of semantic segmentation of acute lymphoblastic cells can be employed in the development of a computer-aided analysis system. In the realm of peripheral blood smear analysis, deep learning methods, particularly convolutional neural networks, are commonly utilized. Currently, transformer-based models have emerged as the state-of-the-art approach for semantic segmentation tasks. In this study, Seg-Former, a transformer-based model for semantic segmentation, was utilized to segment and classify acute lymphoblastic cells using four distinct training strategies. The optimal outcome was achieved with a mean intersection-over-union (IoU) of 0.821 and a mean accuracy of 0.917.