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Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images
Joint Authors
Shahzad, Muhammad
Umar, Arif Iqbal
Khan, Muazzam A.
Shirazi, Syed Hamad
Khan, Zakir
Yousaf, Waqas
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation.
In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach.
We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model.
The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage.
In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed.
After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model.
In the third step, the training process is initiated on the proposed model.
We have evaluated our network performance on three evaluation metrics.
We obtained outstanding results with respect to classwise, as well as global and mean accuracies.
Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.
American Psychological Association (APA)
Shahzad, Muhammad& Umar, Arif Iqbal& Khan, Muazzam A.& Shirazi, Syed Hamad& Khan, Zakir& Yousaf, Waqas. 2020. Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139420
Modern Language Association (MLA)
Shahzad, Muhammad…[et al.]. Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1139420
American Medical Association (AMA)
Shahzad, Muhammad& Umar, Arif Iqbal& Khan, Muazzam A.& Shirazi, Syed Hamad& Khan, Zakir& Yousaf, Waqas. Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139420
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139420