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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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

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Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network.

Khadija Manzoor¹, Nauman U Gilal², Marco Agus¹

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

|July 16, 2025

Summary

This study introduces a dual-stage deep learning framework for accurate skin lesion segmentation and classification. The model shows high performance on diverse datasets, aiding in early skin disease detection and teledermatology.

Keywords:

Skin lesion segmentation deep learning image augmentation skin cancer skin disease classification

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Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare
Computational Pathology

Background:

Skin diseases pose a global health burden, impacting physical and psychosocial well-being.
Early and accurate detection of skin lesions, particularly malignant ones, is crucial for effective treatment and patient outcomes.
Challenges in skin lesion analysis include data imbalance, variability in lesion appearance, and low image contrast.

Purpose of the Study:

To develop and evaluate a dual-stage deep learning framework for precise skin lesion segmentation and classification.
To address the challenges of imbalanced datasets and lesion variability in automated skin disease detection.
To assess the framework's performance on benchmark datasets and a novel, clinically realistic dataset.

Main Methods:

A U-Net model with a VGG16 encoder was employed for precise instance segmentation of skin lesions.
EfficientFormer and SwiftFormer networks were utilized for lesion classification, tested on balanced and imbalanced data.
Experiments were conducted on HAM10000, ISIC 2018, and the ISIC 2024 SLICE-3D datasets, including fusion approaches for the latter.

Main Results:

The EfficientFormerV2 model achieved 97.11% accuracy and a 97.14% F1-score on the balanced HAM10000 dataset.
The segmentation model demonstrated high performance on ISIC 2018, with 97.59% accuracy and 94.24% Dice similarity.
On the ISIC 2024 SLICE-3D dataset, image+tabular fusion achieved a competitive score, highlighting performance in imbalanced, realistic settings.

Conclusions:

The dual-stage deep learning framework exhibits high accuracy and robustness for skin lesion segmentation and classification.
The framework's adaptability to large-scale, non-dermoscopic data like SLICE-3D indicates its potential for real-world applications.
This approach shows promise for improving skin cancer triage and teledermatology services.