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- Title
Segmentation and Classification of White Blood Cells Using the UNet.
- Authors
Alharbi, Amal H.; Aravinda, C. V.; Meng Lin; Venugopala, P. S.; Reddicherla, Phalgunendra; Shah, Mohd Asif
- Abstract
In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. 'ese plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. 'is presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. 'is study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset.'e overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.
- Publication
Contrast Media & Molecular Imaging, 2022, p1
- ISSN
1555-4309
- Publication type
Article
- DOI
10.1155/2022/5913905