Paper Info
Title
Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model
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.
Year
DOI
Venue
2022
10.3390/s22041492
SENSORS
Keywords
DocType
Volume
classification, deep learning, esophageal lesions, gastrointestinal endoscopy, multi-task, segmentation
Journal
22
Issue
ISSN
Citations 
4
1424-8220
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Suigu Tang100.34
Xiaoyuan Yu200.68
Chak-Fong Cheang3314.21
Zeming Hu400.34
Tong Fang500.34
I-Cheong Choi600.34
Hon-Ho Yu700.34