Leveraging Transfer Learning for Object Detection in Medical Imaging

The analysis of medical images is one of the key areas in the diagnosis of many conditions. However, because of the intricacy of medical images and the relatively limited size of many available datasets, traditional machine learning models are usually not as precise. Transfer learning has emerged as an approach to alleviate these problems as it relies on already existing models. This paper explores the latest advancements in transfer learning for medical image analysis, particularly in object detection. Object detection model YOLO (You Only Look Once) and its variants have gained substantial acceptance for their highly efficient architecture and improved accuracy. In this study, YOLOv8-pretrained on COCO dataset and the advanced version in YOLO family- is fine tuned for detection of leukemia cells in microscopic blood images. Model is trained and evaluated on ALL-IDB2 dataset using Google Colab. Data annotation and augmentation are used to improve the model performance. Experimental results are compared with existing state-of-the-art models, demonstrating that YOLOv8 outperform with 99.3% mean Average Precision(mAP). Our proposed model significantly improves accuracy, making real-time detection of leukemia cells more efficient.