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

• URL: http://arxiv.org/abs/2507.19296v1
• Date: Fri, 25 Jul 2025 14:12:05 GMT
• Title: ABCD: Automatic Blood Cell Detection via Attention-Guided Improved YOLOX
• Authors: Ahmed Endris Hasen, Yang Shangming, Chiagoziem C. Ukwuoma, Biniyam Gashaw, Abel Zenebe Yutra,
• Abstract summary: We propose automatic blood cell detection method (ABCD) based on an improved version of YOLOX, an object detector.<n>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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer [0.0]
  Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests.<n>In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells.<n>A novel Deep Learning architecture, the Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios.
  arXiv  Detail & Related papers  (2025-05-21T13:25:27Z)
• A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology [1.607370483729741]
  We evaluate the performance of several Deep Learning (DL) methods for cell detection in Papanicolaou-stained cytological Whole Slide Images (WSIs)<n>We examine recentoff-the-shelf algorithms as well as custom-designed detectors, applying them to two datasets.<n>Results show that centroid-based methods, particularly the Improved Fully Convolutional Regression Network (IFCRN) method, outperform segmentation-based methods in terms of both detection accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-09T15:08:12Z)
• Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes [37.440449828136586]
  Investigation focuses on a novel approach termed DE-ViT.<n>This methodology is employed in a Few-Shot paradigm, wherein training relies on a limited number of images.<n>While DE-ViT has demonstrated state-of-the-art performance on the COCO and LVIS datasets, both baseline models surpassed its performance on the Raabin-WBC dataset.
  arXiv  Detail & Related papers  (2025-03-21T12:46:49Z)
• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• Transforming Blood Cell Detection and Classification with Advanced Deep Learning Models: A Comparative Study [2.3942577670144423]
  This study utilizes a YOLOv10 model trained on Roboflow data with images resized to 640x640 pixels across varying epochs. The results show that increased training epochs significantly enhance accuracy, precision, and recall, particularly in real-time blood cell detection & classification.
  arXiv  Detail & Related papers  (2024-10-21T06:23:13Z)
• Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images [40.347953893940044]
  We introduce a novel approach for white blood cell classification based on neural cellular automata (NCA) Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Our results demonstrate that NCA can be used for image classification, and they address key challenges of conventional methods.
  arXiv  Detail & Related papers  (2024-04-08T14:59:53Z)
• Diagnosing Human-object Interaction Detectors [42.283857276076596]
  We introduce a diagnosis toolbox to provide detailed quantitative break-down analysis of HOI detection models. We analyze eight state-of-the-art HOI detection models and provide valuable diagnosis insights to foster future research.
  arXiv  Detail & Related papers  (2023-08-16T17:39:15Z)
• MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation [1.3295074739915493]
  We propose a multiple attention-guided fusion network (MAF-Net) to accurately detect blood vessels in retinal fundus images. Traditional UNet-based models may lose partial information due to explicitly modeling long-distance dependencies. We show that our method produces satisfactory results compared to some state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-05T15:22:20Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• TE-YOLOF: Tiny and efficient YOLOF for blood cell detection [26.463853328783962]
  Blood cell detection in microscopic images is an essential branch of medical image processing research. In this work, an object detector based on YOLOF has been proposed to detect blood cell objects such as red blood cells, white blood cells and platelets. For increasing efficiency and flexibility, the EfficientNet Convolutional Neural Network is utilized as the backbone for the proposed object detector.
  arXiv  Detail & Related papers  (2021-08-27T14:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.