Acute Lymphoblastic Leukemia Image Classification Performance with Transfer Learning Using CNN Architecture

Abstract

Leukemia is diagnosed by observing two indicators, bone marrow smear and peripheral blood smear with laboratory skills using a microscope for diagnosing cancer. All diagnostics tests require advanced laboratory tests and another limitations like time and pricing. With all limitations, this study compares deep learning architectures from image augmentation from HSV images for diagnosis and classification for four label outputs using Adam optimizer. As a result of this study, VGG16 achieved better evaluation results than another architecture which attained an accuracy, sensitivity, specificity, and validation accuracy of 97.50%, 99.96%, 100%, and 98.44%, respectively. For its development in real cases, the modeling can be applied directly to the relevant in the future or using a new novel method architecture.