ABCD: Automatic Blood Cell Detection via Attention-Guided Improved YOLOX

TL;DR

This paper introduces ABCD, an improved deep learning model based on YOLOX with attention modules and enhanced loss functions, achieving higher accuracy and speed for automatic blood cell detection in microscopic images.

Contribution

The study presents novel modifications to YOLOX, including CBAM, ASFF, and CIOU loss, to improve blood cell detection accuracy and efficiency.

Findings

Achieved 95.49% mAP@0.5 on BCCD dataset.

Improved detection speed by 2.9%.

Outperformed existing methods in accuracy and efficiency.

Abstract

Detection of blood cells in microscopic images has become a major focus of medical image analysis, playing a crucial role in gaining valuable insights into a patient's health. Manual blood cell checks for disease detection are known to be time-consuming, inefficient, and error-prone. To address these limitations, analyzing blood cells using deep learning-based object detectors can be regarded as a feasible solution. In this study, we propose automatic blood cell detection method (ABCD) based on an improved version of YOLOX, an object detector, for detecting various types of blood cells, including white blood cells, red blood cells, and platelets. Firstly, we introduce the Convolutional Block Attention Module (CBAM) into the network's backbone to enhance the efficiency of feature extraction. Furthermore, we introduce the Adaptively Spatial Feature Fusion (ASFF) into the network's neck, which optimizes the fusion of different features extracted from various stages of the network. Finally, to speed up the model's convergence, we substitute the Intersection over Union (IOU) loss function with the Complete Intersection over Union (CIOU) loss function. The experimental results demonstrate that the proposed method is more effective than other existing methods for BCCD dataset. Compared to the baseline algorithm, our method ABCD achieved 95.49 % mAP@0.5 and 86.89 % mAP@0.5-0.9, which are 2.8% and 23.41% higher, respectively, and increased the detection speed by 2.9%, making it highly efficient for real-time applications.