BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells. (April 2021)

• Record Type: Journal Article
• Authors: Long, Fei; Peng, Jing-Jie; Song, Weitao; Xia, Xiaobo; Sang, Jun
• Is Part Of: Computer methods and programs in biomedicine. Volume 202(2021)
• Publication Date: 2021-04
• Subjects: Capsule Networks -- CNN -- Blood Cells -- Image Classification -- Deep Learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.105972
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a novel model, BloodCaps, to improve bloodcell feature extraction and classification BloodCaps proved superior against AlexNet and VGG16 in low resolution dataset, small sample dataset, and low resolution small sample dataset Through extensive experiments, we demonstrate the effectiveness of BloodCaps in octal bloodcell classification. Abstract: Background and Objective: The classification of human peripheral blood cells yields significance in the detection of inflammation, infections and blood cell disorders such as leukemia. Limitations in traditional algorithms for blood cell classification and increased computational processing power have allowed machine learning methods to be utilized for this clinically prevalent task. Methods: In the current work, we present BloodCaps, a capsule based model designed for the accurate multiclassification of a diverse and broad spectrum of blood cells. Results: Implemented on a large-scale dataset of 8 categories of human peripheral blood cells, the proposed architecture achieved an overall accuracy of 99.3%, outperforming convolutional neural networks such as AlexNet(81.5%), VGG16(97.8%), ResNet-18(95.9%) and InceptionV3(98.4%). Furthermore, we devised three new datasets(low-resolution dataset, small dataset, and low-resolution small dataset) from the original dataset, and tested BloodCaps in comparison with AlexNet, VGG16, ResNet-18, and InceptionV3. To further validate the applicability of our proposed model, we tested BloodCaps on additional public datasets such as the All IDB2, BCCD, and Cell Vision datasets. Compared with the reported results, BloodCaps showed the best performance in all three scenarios. Conclusions: The proposed method proved superior in octal classification among all three datasets. We believe the proposed method represents a promising tool to improve the diagnostic performance of clinical blood examinations.