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TriDermCancerNet: A hybrid deep learning framework for skin cancer classification.

Bushra Fiaz¹, Muhammad Attique Khan², Afia Zafar³

¹Department of Computer Engineering, HITEC University, Pakistan.

The Journal of International Medical Research

|June 23, 2026

Summary

Related Concept Videos

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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This summary is machine-generated.

A novel Tri Model Dermatology Cancer Neural Network (TriDermCancerNet) significantly improves skin cancer classification from dermoscopic images. This advanced deep learning model achieves high accuracy, aiding in early skin cancer detection.

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Automated skin cancer diagnosis from dermoscopic images is challenging due to poor image quality, similar lesion appearances, and imbalanced datasets.
Existing methods struggle with variability and interclass similarities in skin lesion classification.

Purpose of the Study:

To propose a novel Tri Model Dermatology Cancer Neural Network (TriDermCancerNet) for accurate skin cancer classification.
To address challenges including dataset variability, interclass similarity, and model explainability in skin cancer diagnosis.

Main Methods:

Utilized International Skin Imaging Collaboration 2018 and 2019 datasets.
Applied contrast enhancement and data augmentation for image preprocessing and dataset balancing.

Keywords:

Skin cancer bottleneck mechanism classification deep learning dermoscopic images information fusion

Related Experiment Videos

Developed TriDermCancerNet integrating Inception, Inverted Bottleneck Residual, and Dense modules in parallel, with feature fusion via depth concatenation.

Optimized hyperparameters using Bayesian optimization.

Main Results:

The fused TriDermCancerNet achieved 98.6% accuracy and 1.0 AUC on the ISIC 2018 dataset.
Achieved 99.7% accuracy and 1.0 AUC on the ISIC 2019 dataset.
Statistical testing confirmed the superiority of the fused model over individual branches (p < 0.05).

Conclusions:

The proposed TriDermCancerNet offers a precise and robust framework for skin cancer classification.
This hybrid deep learning approach serves as a valuable diagnostic aid for clinicians in early skin cancer detection.