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

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Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame

Muhammad Attique Khan¹, Muhammad Sharif¹, Tallha Akram²

¹Department of Computer Science, Wah Campus, COMSATS University Islamabad, Wah Cantonment 47040, Pakistan.

Diagnostics (Basel, Switzerland)

|May 5, 2021

Summary

This summary is machine-generated.

This study introduces an automated method for skin cancer diagnosis, enhancing accuracy in classifying skin lesions using deep learning and feature selection. The approach achieves high segmentation and classification performance on multiple datasets.

Keywords:

deep features feature fusion heuristic feature optimization melanoma moth flame optimization skin cancer

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Published on: March 3, 2023

Area of Science:

Dermatology
Computer Vision
Artificial Intelligence

Background:

Manual skin cancer diagnosis is slow and costly.
Automated methods are needed for accurate multiclass skin lesion classification.

Purpose of the Study:

To develop a fully automated approach for skin lesion segmentation and classification.
To improve diagnostic accuracy using deep features and optimized selection.

Main Methods:

Image enhancement using local color-controlled histogram intensity values (LCcHIV).
Deep Saliency Segmentation with a custom 10-layer CNN for saliency estimation.
Feature extraction via a deep pre-trained CNN, followed by improved moth flame optimization (IMFO) for feature selection.
Feature fusion using multiset maximum correlation analysis (MMCA) and classification with Kernel Extreme Learning Machine (KELM).

Main Results:

High segmentation accuracy achieved across multiple datasets: ISBI 2016 (95.38%), ISBI 2017 (95.79%), ISIC 2018 (92.69%), and PH2 (98.70%).
Classification accuracy of 90.67% on the HAM10000 dataset.
Demonstrated effectiveness through comparison with state-of-the-art techniques.

Conclusions:

The proposed automated method offers a robust and accurate solution for multiclass skin lesion segmentation and classification.
The integration of deep features, optimized selection, and advanced classification techniques significantly enhances diagnostic capabilities.