LeukoVision: Improving Leukemia Diagnosis with VGG16 Convolutional Neural Network

Abstract

This study explores the application of the Visual Geometry Group 16 (VGG-16) Convolutional Neural Network (CNN) to enhance leukemia diagnosis. Through deep learning techniques, particularly transfer learning, we investigate VGG16's potential in accurately categorizing leukemia from blood smear images. Our findings demonstrate that the fine-tuned VGG16 model achieves an accuracy of 79.75% in leukemia classification, surpassing existing methods. Additionally, our comparative analysis highlights VGG16's superior performance in identifying different types of white blood cells associated with leukemia. This research contributes to advancing medical imaging and offers clinicians a reliable tool for informed decision-making in leukemia detection.