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Updated: Jul 9, 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

Brain tumor feature extraction and edge enhancement algorithm based on U-Net network.

Dapeng Cheng^1,2, Xiaolian Gao¹, Yanyan Mao¹

¹School of Computer Science and Technology, Shandong Business and Technology University, No.191 Binhai Middle Road, Yantai City, Shandong Province, Yantai, 264000, China.

|November 30, 2023

Summary

This summary is machine-generated.

This study introduces EAV-UNet, an advanced system for precise brain tumor segmentation, significantly improving detection of hazy lesion margins. The EAV-UNet model enhances feature extraction and edge detection, leading to more accurate tumor region identification.

Keywords:

Attention mechanism Edge detection U-Net VGG-19

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain tumors pose a significant challenge, with over 100,000 annual fatalities.
Diverse lesion morphology and hazy boundaries complicate accurate segmentation prediction.

Purpose of the Study:

To introduce EAV-UNet, a novel system for accurate brain tumor lesion detection and segmentation.
To optimize feature extraction and enhance segmentation for challenging cases with hazy tumor margins.

Main Methods:

Incorporated VGG-19 into the U-Net encoder for a deeper network structure.
Integrated attention mechanisms (CBAM) and an edge detection module to enhance feature and edge information extraction.

Main Results:

EAV-UNet demonstrated significant improvements across evaluation metrics on multiple datasets.
Achieved high precision (up to 95.3%) and F1 scores (up to 96.1%), with reduced Hausdorff distances (as low as 1.31).
Consistently produced segmentations similar to original images, especially for low-contrast and blurry lesion edges.

Conclusions:

The refined EAV-UNet architecture, with smaller kernels and integrated attention/edge modules, enhances brain tumor segmentation accuracy.
The system effectively reinforces edge information and boosts attention scores for improved lesion identification.