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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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MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information

Sobia Bibi¹, Muhammad Attique Khan^2,3, Jamal Hussain Shah¹

¹Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan.

Diagnostics (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning model for accurate skin cancer and melanoma detection. The novel approach enhances image preprocessing, feature extraction, and selection, outperforming existing methods for early diagnosis.

Keywords:

classification contrast enhancement deep learning feature selection fusion marine predator optimization skin cancer

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin cancer, especially melanoma, is a major global health concern with high mortality rates.
Early detection of melanoma is crucial for successful treatment outcomes.
Artifacts in medical images complicate accurate lesion detection and classification.

Purpose of the Study:

To develop a deep learning architecture for multiclass skin cancer and melanoma detection.
To improve the accuracy and efficiency of automated skin lesion analysis.
To address challenges posed by image artifacts in skin cancer diagnosis.

Main Methods:

A four-step deep learning architecture: image preprocessing, feature extraction/fusion, feature selection, and classification.
Novel contrast enhancement using image luminance information.
Transfer learning with modified DarkNet-53 and DensNet-201 models, optimized by a Genetic Algorithm for hyperparameters.
Feature fusion via serial-harmonic mean and selection using Marine Predator Optimization (MPA) controlled Reyni Entropy.
Classification using machine learning algorithms on selected features.

Main Results:

The proposed deep learning framework achieved maximum accuracies of 85.4% on the ISIC2018 dataset and 98.80% on the ISIC2019 dataset.
The developed methods demonstrated superior performance compared to several recent techniques.
The combination of advanced preprocessing, feature engineering, and optimized deep models led to high classification accuracy.

Conclusions:

The proposed deep learning architecture offers a robust and effective solution for multiclass skin cancer and melanoma detection.
The novel feature selection and fusion techniques significantly enhance diagnostic accuracy.
This framework has the potential to aid clinicians in early and precise diagnosis of skin cancer.