1. Histopathological image recognition of breast cancer based on three-channel reconstructed color slice feature fusion.
- Author
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Zhang, Cheng, Bai, Yanping, Yang, Can, Cheng, Rong, Tan, Xiuhui, Zhang, Wendong, and Zhang, Guojun
- Subjects
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IMAGE recognition (Computer vision) , *BREAST cancer , *COMPUTER-aided diagnosis , *TEXT recognition , *CONVOLUTIONAL neural networks , *CANCER diagnosis , *HISTOPATHOLOGY - Abstract
Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%–99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC. • Three-channel reconstructed slices contain better color features of breast cancer. • Reconstructed B-channel features contain more information about the cell nucleus. • Using color features to diagnose breast cancer can avoid segmentation of nuclei. • Reconstructed color slices enable low-complexity model to identify breast cancer. • Considering recognition accuracy and speed, feature fusion model is more suitable for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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