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ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Zhan Wu 1 Rongjun Ge 2, 1 Minli Wen 1 Gaoshuang Liu 3 Yang Chen 1, 4, * Pinzheng Zhang 2 Xiaopu He 3 Jie Hua 3 Limin Luo 5, 4 Shuo Li 6
* Corresponding author
1 Southeast University [Jiangsu]
2 School of Computer Engineering and Science [Shanghai]
3 Nanjing Medical University
4 CRIBS - Centre de Recherche en Information Biomédicale sino-français
5 Jiangsu Normal University
6 UWO - University of Western Ontario
Abstract : Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification and lesion-specific segmentation network is proposed for automatic esophageal lesion annotation at pixel level. For the established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718, and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655, and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate, and reliable esophageal lesion diagnosis in clinics.
Keywords : Convolutional neural network (CNN) Deep learning Dual-stream esophageal lesion classification Esophageal lesions Index Terms-Esophageal lesions deep learning dual-stream esophageal lesion classification convolutional neural network (CNN)
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https://hal.archives-ouvertes.fr/hal-02998748
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Submitted on : Thursday, November 19, 2020 - 2:25:40 PM
Last modification on : Friday, January 15, 2021 - 3:33:28 AM

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UNIV-RENNES1 | UNIV-RENNES | CRIBS | LTSI

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Zhan Wu, Rongjun Ge, Minli Wen, Gaoshuang Liu, Yang Chen, et al.. ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.. Medical Image Analysis, Elsevier, 2021, 67, pp.101838. ⟨10.1016/j.media.2020.101838⟩. ⟨hal-02998748⟩

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