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An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification.

Muhammad Fiaz^1,2, Muhammad Bilal Shoaib Khan¹, Abdul Hannan Khan¹

¹Department of Computer Science, Green International University, Lahore, Pakistan.

Frontiers in Medicine

|October 29, 2025

Summary

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This summary is machine-generated.

This study introduces a hybrid deep learning model for skin lesion segmentation and classification from dermoscopic images. The AI tool achieves high accuracy, aiding dermatologists in diagnosing skin conditions.

Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin disease diagnosis is challenging due to visual complexity and subjective manual examination.
Malignant tumors like melanoma require accurate and timely diagnosis.
Deep learning offers potential for objective and accurate skin lesion analysis.

Purpose of the Study:

To develop a hybrid deep learning framework for simultaneous skin lesion segmentation and multi-class classification.
To enhance model interpretability and clinical trust using explainable AI (XAI).
To improve diagnostic accuracy for various skin conditions using dermoscopic images.

Main Methods:

A dual-task architecture combining U-Net for segmentation and EfficientNet-B0 for classification was developed.

Keywords:

Grad-CAM classification explainable AI segmentation skin disease

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Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated for model interpretability.

The model was trained and validated on the HAM10000 dataset.

Main Results:

The model achieved a Dice coefficient above 0.85 for segmentation.
Classification accuracy reached approximately 85%, demonstrating robust performance.
Reliable results were obtained across diverse skin lesion types, despite class imbalance.

Conclusions:

The hybrid deep learning model shows significant promise for accurate skin lesion segmentation and classification.
Explainable AI (XAI) integration enhances transparency, crucial for clinical adoption.
This approach can support dermatologists, particularly in resource-limited settings, by improving diagnostic efficiency and accuracy.