Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image

Abstract

Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of mAP@.5 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.