Integrating Custom GAN Segmentation with Advanced Deep Learning Classifiers for Enhanced Acute Lymphoblastic Detection

Acute Lymphoblastic Leukemia (ALL) is a highly aggressive form of cancer that originates in the bone marrow and is characterized by uncontrolled growth of immature white blood cells. The proposed work utilizes the various public single-cell datasets and employs Generative Adversarial Networks to generate synthetic images (DCGAN) as well as GAN for semantic segmentation achieving an accuracy of 98.79% for healthy cell segmentation and 99.59% for blast cell segmentation. The work further explores the impact of segmentation on classification accuracy, integrating three classification models (RegNet-Y, ConvNeXt, Vision Transformer). Without segmentation, these models achieve accuracies of 67.16%, 82.19%, and 86.76%, respectively. Using the GAN segmentation outputs, notable improvements in accuracy are observed, with results reaching 72.63%, 93.79%, and 90.77%. This highlights the benefits of combining precise segmentation and robust classification models.