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Updated: Dec 3, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

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

Zhan Wu¹, Rongjun Ge², Minli Wen¹

¹School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China.

Medical Image Analysis

|October 31, 2020

Summary

This summary is machine-generated.

A new Esophageal Lesion Network (ELNet) uses deep learning for automatic esophageal lesion classification and segmentation. This AI-driven approach enhances diagnostic accuracy and efficiency in clinical settings.

Keywords:

Convolutional neural network (CNN)Deep learning Dual-stream esophageal lesion classification Esophageal lesions

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Esophageal lesion classification and segmentation are crucial for disease management.
Current clinical methods face challenges due to individual variations and visual similarities, leading to risks and time consumption.

Purpose of the Study:

To develop an automated system for esophageal lesion classification and segmentation.
To improve the accuracy and efficiency of esophageal lesion diagnosis.

Main Methods:

Proposed an Esophageal Lesion Network (ELNet) utilizing deep convolutional neural networks (DCNNs).
Integrated dual-view contextual information for feature extraction.
Developed a lesion-specific segmentation network for pixel-level annotation.
Validated the method on a large-scale database of 1051 white-light endoscopic images using ten-fold cross-validation.

Main Results:

Achieved high performance in classification: sensitivity 0.9034, specificity 0.9718, accuracy 0.9628.
Demonstrated strong performance in segmentation: sensitivity 0.8018, specificity 0.9655, accuracy 0.9462.
The framework proved efficient, accurate, and reliable for clinical diagnosis.

Conclusions:

The ELNet provides an effective solution for automatic esophageal lesion analysis.
The proposed deep learning framework significantly enhances the reliability of esophageal disease diagnosis.
This technology promises to streamline clinical workflows and improve patient outcomes.