The Evaluation of Acute Myeloid Leukaemia (AML) Blood Cell Detection Models Using Different YOLO Approaches

Abstract

This study proposes to evaluate the performance of Acute Myeloid Leukaemia (AML) blast cell detection models in microscopic examination images for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as You Only Look Once (YOLO) developed for object detection is the successful state-of-the-art algorithms in real-time object detection systems. We employ four versions of the YOLO algorithm: YOLOv3, YOLOv3-Tiny, YOLOv2 and YOLOv2-Tiny for detection of 15-class of AML blood cells in examination images. We also acquired the publicly available dataset from The Cancer Imaging Archive (TCIA), which consists of 18,365 expert-labelled single-cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that four types of YOLO approach have outstanding performances of more than 92% in precision and sensitivity. In comparison, YOLOv3 has more reliable performance than the other three approaches. Consistently, the AUC values for the four YOLO models are 0.969 (YOLOv3), 0.967 (YOLOv3-Tiny), 0.963 (YOLOv2), and 0.948 (YOLOv2-Tiny). Furthermore, we compare the best model's performance between approaches that use the entire training dataset without using data augmentation techniques and image division with data augmentation techniques. Remarkably, by using 33.51 percent of the training data in model training, the prediction outcomes from the model that used image partitioning with data augmentation were similar to those obtained using the complete training dataset. This work potentially provides a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.