ArticleBMC bioinformatics2022
Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Hybrid deep learning ensemble model for detecting small to medium rotator cuff tears from shoulder radiographs.Journal of orthopaedic surgery and research · 2026Article
- Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.Bone & joint research · 2026Article
- Article
- Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging.Health information science and systems · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
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3 authors.
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Abstract
backgroundResearchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem.
resultsThe three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example.
conclusionIn blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.
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