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

arXiv: https://arxiv.org/abs/2507.19296 · HTML: https://mlsanity.com/paper/2507.19296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)
- [IoU Loss for 2D/3D Object Detection](https://mlsanity.com/paper/1908.03851/) (2019)
- [PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions](https://mlsanity.com/paper/2204.12511/) (2022)
- [Learning Spatial Fusion for Single-Shot Object Detection](https://mlsanity.com/paper/1911.09516/) (2019)
- [Learning to ignore: rethinking attention in CNNs](https://mlsanity.com/paper/2111.05684/) (2021)
- [TE-YOLOF: Tiny and efficient YOLOF for blood cell detection](https://mlsanity.com/paper/2108.12313/) (2021)
- [Deep CNNs for Peripheral Blood Cell Classification](https://mlsanity.com/paper/2110.09508/) (2021)
- [RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images](https://mlsanity.com/paper/2502.02850/) (2025)
- [Detecting Small Objects in Thermal Images Using Single-Shot Detector](https://mlsanity.com/paper/2108.11101/) (2021)

## Abstract (arXiv)

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.