YOLO-Based Generalized Framework for Leukemia Cell Detection Using Unified Microscopic Image Datasets

Abstract

Acute lymphoblastic leukemia is a kind of blood cancer that attacks the lymphoblast, a subgroup of white blood cells. Leukemia is a potentially lethal hematological cancer that requires prompt diagnosis. A skilled manual blood smear examination is one of the laborious and prone to human error conventional diagnostic methods. Although current automated methods developed by researchers use either single-cell or multi-cell pictures to detect leukemia cells, they frequently lack model generalization that perform better on heterogeneous datasets. They are also insufficient for deployment in real time. This study aims to develop generalized real-time system for detecting ALL cells from single and multi-cell microscopic blood smear images. The system utilizes three YOLO based state-of-the-art models: YOLO11, YOLOv8 and YOLOv5. The core novelty of this study lies in the creation of a unified dataset that integrates both single-cell and multi-cell microscopic blood smear images, this enables the model to learn generalized representations from diverse image contexts. Three datasets are merged to create the unified dataset, ALL-IDB1: multi- cell images, ALL-IDB2 & C-NMC-19: single-cell images. Image annotation and preprocessing are performed using Roboflow platform, while Google Colab is used for training and testing. These models are trained separately on individual datasets and the unified dataset.The performance of generalized YOLO models is assessed and contrasted against dataset-specific models using mAP@50 and recall metrics on the same set of unseen images from all three datasets.The experimental results indicate that generalized YOLOv8 model achieved notably high recall and competitive map@50, demonstrating strong adaptability and accuracy. These results highlight YOLOv8 as a promising solution for developing generalized model for leukemia cell detection.