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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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Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.

Atheer Bassel¹, Amjed Basil Abdulkareem², Zaid Abdi Alkareem Alyasseri^3,4,5

¹Computer Center, University of Anbar, Al-Anbar 31001, Iraq.

Diagnostics (Basel, Switzerland)

|October 27, 2022

Summary

This study introduces a machine learning approach for skin cancer classification. A stacked classifier model using Xception feature extraction achieved 90.9% accuracy in identifying melanoma and benign skin cancers.

Keywords:

CNN deep learning machine learning prediction skin cancer

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Area of Science:

Dermatology and Computational Pathology
Artificial Intelligence in Medicine
Digital Health Technologies

Background:

Skin cancer incidence is rising, necessitating efficient diagnostic tools.
Traditional skin cancer identification methods are time-consuming and costly.
Digital technologies and machine learning offer automated classification solutions.

Purpose of the Study:

To develop and evaluate a robust machine learning model for classifying melanoma and benign skin cancers.
To compare the performance of different feature extraction techniques within a stacked classifier framework.
To enhance the accuracy and reliability of automated skin cancer diagnosis.

Main Methods:

A stacked classifier model was developed using three-fold cross-validation.
Feature extraction was performed using deep learning models: ResNet50, Xception, and VGG16.
The system was trained and tested on 1000 skin images, categorizing them as melanoma or benign.

Main Results:

The proposed stacked classifier with Xception feature extraction achieved 90.9% accuracy.
Performance was evaluated using accuracy, F1 scores, AUC, and sensitivity.
Xception demonstrated superior performance compared to ResNet50 and VGG16 for this task.

Conclusions:

The stacked classifier approach shows promise for accurate skin cancer classification.
Xception is an effective feature extraction method for this dermatological application.
Further optimization with larger datasets could lead to a highly reliable skin cancer classification system.