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Related Experiment Video

Updated: May 1, 2026

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

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Published on: November 30, 2022

MSDTCN-Net: A Multi-Scale Dual-Encoder Network for Skin Lesion Segmentation.

Da Li¹, Xinyang Wu¹, Qin Wei¹

¹School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Diagnostics (Basel, Switzerland)

|November 27, 2025

Summary

MSDTCN-Net improves skin cancer detection by accurately segmenting skin lesions. This novel dual-encoder network enhances early diagnosis by capturing both local details and global context for complex lesion shapes.

Keywords:

convolutional neural networks hierarchical feature transfer multi-scale receptive field skin lesion segmentation transformer

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Published on: August 8, 2025

Area of Science:

Medical image analysis
Computer-aided diagnosis
Dermatology

Background:

Accurate skin lesion segmentation is crucial for early skin cancer detection.
Convolutional Neural Networks (CNNs) struggle with long-range dependencies, impacting performance on complex lesion shapes.

Purpose of the Study:

To develop an advanced deep learning model for precise skin lesion segmentation.
To overcome the limitations of traditional CNNs in capturing global context and complex lesion morphology.

Main Methods:

Proposed MSDTCN-Net, a dual-encoder network integrating ConvNeXt and Deformable Transformer for local and global feature extraction.
Incorporated Squeeze-and-Excitation (SE) for channel emphasis, Multi-Scale Receptive Field (MSRF) for scale variation, and Hierarchical Feature Transfer (HFT) for decoder enhancement.

Main Results:

MSDTCN-Net demonstrated competitive performance on ISIC 2016, 2017, 2018, and PH2 datasets.
Achieved high accuracy in skin lesion segmentation, validated by IoU, Dice, and ACC metrics.

Conclusions:

MSDTCN-Net effectively integrates local/global features, multi-scale adaptability, and semantic guidance for high-accuracy segmentation.
The model shows significant potential for improving clinical diagnostic applications in dermatology.