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Proceedings Paper

PColorNet: investigating the impact of different color spaces for pathological image classification
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Paper Abstract

In the field of computer vision and medical imaging, the classification is one of the common approaches apply to solve different problems. There are several Deep Learning (DL) models have been proposed in the last few years and applied for classification tasks and shown significantly better performance compared to existing methods. In particular, the DL methods have been applied and achieved state-of-the-art performance for classification, segmentation, and detection tasks in the field of medical imaging. In the field of digital pathology, very large dimensional images are captured with microscopes which are significantly higher in dimension and different (texture, color, etc.) compared to the images are used for analysis in computer vision tasks. As the computational pathology becomes very promising area of research, it is very important to study the impact of different color spaces for pathological image classification problems. In this paper, we have considered six different color spaces including RGB, CIE, HSV, YCbCr, Lab, and HSL for histopathology tissue classification tasks where the Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model is applied. The KIMIA Path960 database is used for evaluating the model in this implementation. The RGB color space shows the highest testing performance for tissue classification task which is around 0. 73% and 1.13% better compared to the performance of Lab and CIE color spaces respectively and this performance is significantly higher than the testing accuracy of the model in others color spaces. In addition, the highest performance for the proposed model is around 0.5 percent better compared to existing methods. This study will provide a clear guidance in advance for implementing classification tasks in digital pathology.I n the field of computer vision and medical imaging, the classification is one of the common approaches apply to solve different problems. There are several Deep Learning (DL) models have been proposed in the last few years and applied for classification tasks and shown significantly better performance compared to existing methods. In particular, the DL methods have been applied and achieved state-of-the-art performance for classification, segmentation, and detection tasks in the field of medical imaging. In the field of digital pathology, very large dimensional images are captured with microscopes which are significantly higher in dimension and different (texture, color, etc.) compared to the images are used for analysis in computer vision tasks. As the computational pathology becomes very promising area of research, it is very important to study the impact of different color spaces for pathological image classification problems. In this paper, we have considered six different color spaces including RGB, CIE, HSV, YCbCr, Lab, and HSL for histopathology tissue classification tasks where the Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model is applied. The KIMIA Path960 database is used for evaluating the model in this implementation. The RGB color space shows the highest testing performance for tissue classification task which is around 0. 73% and 1.13% better compared to the performance of Lab and CIE color spaces respectively and this performance is significantly higher than the testing accuracy of the model in others color spaces. In addition, the highest performance for the proposed model is around 0.5 percent better compared to existing methods. This study will provide a clear guidance in advance for implementing classification tasks in digital pathology.

Paper Details

Date Published: 16 March 2020
PDF: 11 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113201A (16 March 2020); doi: 10.1117/12.2550046
Show Author Affiliations
Shamima Nasrin, Univ. of Dayton (United States)
Md. Zahangir Alom, Univ. of Dayton (United States)
Tarek M. Taha, Univ. of Dayton (United States)
Vijayan K. Asari, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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