Leukemia Detection using YOLOv8: A Deep Learning Approach

Abstract

Leukemia is a blood cancer characterized by abnormal proliferation of white blood cells. Early detection is crucial for improving survival rates. This paper presents the application of YOLOv8, a state-of-the-art object detection model, for detecting leukemic cells in microscopic blood smear images. The methodology includes dataset preprocessing, data augmentation, model training, and evaluation using benchmark datasets. Experimental results demonstrate YOLOv8’s high accuracy and real-time detection capability, highlighting its potential for clinical implementation.