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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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Explainable deep learning for skin cancer detection using swish-activated convolutional networks.

Subhayan Mukherjee¹, Khushbu Chandrakar², Subrata Chowdhury³

¹Department of Artificial Intelligence & Machine Learning, Asansol Engineering College, Asansol, India.

Discover Oncology

|January 10, 2026

Summary

This study introduces a deep convolutional neural network (DCNN) for accurate skin cancer diagnosis, achieving over 98% accuracy. Explainable AI (XAI) ensures transparency, aiding physicians in early and precise detection of skin lesions.

Keywords:

AI in Healthcare Deep convolutional neural network (DCNN)Explainable artificial intelligence (XAI)Gradient-weighted class activation mapping (Grad-CAM)Local interpretable model-agnostic explanations (LIME)

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin cancer diagnosis faces challenges due to complex visual patterns and subjective manual inspection.
Conventional methods are time-consuming, prone to misinterpretation, and yield high false positive rates.

Purpose of the Study:

To develop a novel deep convolutional neural network (DCNN) architecture for accurate and interpretable skin cancer diagnosis.
To integrate explainable artificial intelligence (XAI) techniques for enhanced transparency and reliability in clinical decision-making.

Main Methods:

A unique DCNN architecture utilizing the Swish activation function was developed to analyze skin lesion datasets.
Multiple localized and global explainable artificial intelligence (XAI) approaches were employed to evaluate model predictions.

Main Results:

The DCNN model achieved high performance metrics: 98.31% accuracy, 98.12% precision, 98.01% recall, and 98.09% F1-score.
XAI methods provided a comprehensible framework for medical practitioners to assess the model's reasoning.

Conclusions:

The integration of deep learning with XAI offers a reliable mechanism for early and precise skin cancer identification.
The study highlights the importance of transparency and reliability in AI-driven medical diagnostics to bridge the gap between research and healthcare application.