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. 2022 Feb 15;22(4):1492.
doi: 10.3390/s22041492.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model

Suigu Tang  1 , Xiaoyuan Yu  1 , Chak-Fong Cheang  1 , Zeming Hu  1 , Tong Fang  1 , I-Cheong Choi  2 , Hon-Ho Yu  2
Affiliations

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model

Suigu Tang et al. Sensors (Basel). .

Abstract

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

Keywords: classification; deep learning; esophageal lesions; gastrointestinal endoscopy; multi-task; segmentation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow diagram for the training set and validation set of the MTCS model.
Figure 2
Figure 2
The diagnostic procedure of the MTCS model.
Figure 3
Figure 3
(a) Architecture of ELCNet; (b) architecture of ELSNet.
Figure 4
Figure 4
Receiver operating characteristic of ELCNet and other methods.
Figure 5
Figure 5
The confusion matrix of ELCNet.
Figure 6
Figure 6
(a–f) Comparison of cancer segmentation between ELSNet and other methods.
See this image and copyright information in PMC

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