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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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Summary

This summary is machine-generated.

This study introduces a deep learning (DL) model for precise skin cancer diagnosis. The automated system enhances image quality and classifies lesions, improving early detection rates.

Keywords:

ESRGAN HAM10000 convolutional neural network deep learning machine learning skin lesion

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin cancer is a prevalent global health concern, with early diagnosis crucial for effective treatment.
Deep learning (DL) shows promise for automated diagnostic systems in healthcare.
Existing methods for skin lesion analysis can be improved for accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a DL-based automated system for precise skin lesion segmentation and classification.
To enhance medical professionals' capabilities in diagnosing various types of skin cancer.
To improve the accuracy and efficiency of skin cancer diagnosis through advanced image analysis.

Main Methods:

Image enhancement using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN).
Segmentation of Regions of Interest (ROI) for lesion localization.
Data augmentation to address data imbalance.
Classification of skin lesions using a Convolutional Neural Network (CNN) and a modified Resnet-50 model on the HAM10000 dataset.

Main Results:

The proposed CNN-based model achieved an accuracy of 0.86, precision of 0.84, recall of 0.86, and an F-score of 0.86.
The automated system demonstrated superior performance compared to previous studies.
The method effectively segmented lesion zones and classified seven types of skin cancer.

Conclusions:

The developed DL model offers an improved automated approach for skin cancer diagnosis.
This system can aid medical professionals in making more accurate and timely diagnoses.
The enhanced automated method has the potential to benefit both healthcare providers and patients through earlier and more precise detection.