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Related Experiment Video

Updated: Jun 23, 2025

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

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

Published on: November 30, 2022

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial

M A Rasel¹, Sameem Abdul Kareem¹, Zhenli Kwan²

¹Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.

Computers in Biology and Medicine

|June 21, 2024

Summary

This summary is machine-generated.

This study introduces a novel Deep Convolutional Neural Network (DCNN) for detecting the blue-white veil (BWV) in skin lesions, a key indicator of melanoma. The DCNN significantly improves early melanoma diagnosis accuracy.

Keywords:

Bluish veil DCNN Dermoscopic image LIME Melanoma

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Area of Science:

Dermatology and Artificial Intelligence
Medical Imaging Analysis
Oncology

Background:

Melanoma is a deadly skin cancer with thousands of global fatalities.
The blue-white veil (BWV) is a critical diagnostic feature for melanoma.
Limited research exists on automated BWV detection in dermatological images.

Purpose of the Study:

To develop and evaluate a Deep Convolutional Neural Network (DCNN) for accurate BWV detection in skin lesions.
To improve early melanoma diagnosis through enhanced BWV identification.
To utilize explainable artificial intelligence (XAI) for model interpretability.

Main Methods:

A non-annotated dataset was converted to an annotated one using a novel imaging algorithm with color threshold techniques.
A DCNN was designed with custom layers and trained on individual and combined dermoscopic datasets.
An explainable artificial intelligence (XAI) algorithm was applied to interpret the DCNN's decision-making process.

Main Results:

The proposed DCNN achieved high testing accuracies: 85.71% (PH2), 95.00% (ISIC), 95.05% (combined), and 90.00% (Derm7pt).
The DCNN outperformed conventional BWV detection models across various datasets.
The integration of XAI provided insights into the DCNN's BWV detection mechanism.

Conclusions:

The developed DCNN coupled with XAI significantly enhances BWV detection in skin lesions.
This approach offers a robust tool for improving early melanoma diagnosis.
The study highlights the potential of AI in dermatological image analysis for cancer detection.