Segmentation of leukocyte by semantic segmentation model: A deep learning approach
•DeepLab architecture is used to segment leukocytes from microscopic blood images.•It has a higher mean accuracy of 96.1% and a mean intersectionover-union of 92.1%.•The suggested algorithm modeled segmentation as binary classification.•It attains good accuracy with limited data sets available in bi...
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| Published in: | Biomedical signal processing and control Vol. 65; p. 102385 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01-03-2021
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •DeepLab architecture is used to segment leukocytes from microscopic blood images.•It has a higher mean accuracy of 96.1% and a mean intersectionover-union of 92.1%.•The suggested algorithm modeled segmentation as binary classification.•It attains good accuracy with limited data sets available in biomedical applications.
In diagnostic research, analysis of blood micrographs has emerged as one of the relevant techniques for identifying various blood-related diseases. Analysis of white blood cells using computer-aided techniques aids the pathologist to promote accurate diagnosis and early detection of blood diseases. An automated white blood cell analysis system involves cell segmentation, feature extraction, and classification, and its performance depends upon the accuracy of cell segmentation. Accurate and automatic segmentation of leukocyte remains a difficult task because of the complex nature of cell images, staining techniques, and imaging conditions. Here, we employ a semantic segmentation technique that uses a deep learning network to segment leukocyte from microscopic blood images accurately. The proposed model uses DeepLabv3+ architecture with ResNet-50 as a feature extractor network. The experiments have been carried out on three different public datasets consisting of five categories of white blood cells, and 10-fold cross-validation is performed to assert the model's effectiveness. The average segmentation accuracy achieved throughout the suggested network is 96.1% and 92.1% intersection- over-union, which is more than different approaches to supervised learning. Experimental results reveal that the suggested model performs better than other techniques and is appropriate for hematological analysis. |
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| ISSN: | 1746-8094 1746-8108 |
| DOI: | 10.1016/j.bspc.2020.102385 |