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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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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Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.

Fawaz Waselallah Alsaade1, Theyazn H H Aldhyani2, Mosleh Hmoud Al-Adhaileh3

  • 1College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi Arabia.

Computational and Mathematical Methods in Medicine
|May 31, 2021
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This summary is machine-generated.

This study developed advanced artificial intelligence systems for skin cancer detection using deep learning and machine learning. The artificial neural network model achieved high accuracy, outperforming other methods in diagnosing melanoma from dermoscopy images.

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Area of Science:

  • Biomedical imaging and analysis
  • Artificial intelligence in healthcare
  • Dermatology and cancer diagnostics

Background:

  • Computerized biomedical imaging offers significant benefits for diagnosing skin lesions.
  • Modern diagnostic systems and computer-aided diagnosis (CAD) using artificial intelligence are crucial for early melanoma detection.

Purpose of the Study:

  • To develop a novel system for skin cancer diagnosis with a high detection rate.
  • To compare the performance of deep learning and traditional machine learning algorithms for melanoma classification.

Main Methods:

  • Developed two systems: a feature-based system using Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) with Artificial Neural Networks (ANNs), and a deep learning system using Convolutional Neural Networks (CNNs) with AlexNet and ResNet50 transfer learning.
  • Utilized dermoscopy images from the PH2 and ISIC 2018 datasets for system training and evaluation.
  • Employed active contour method for lesion segmentation and standard metrics (accuracy, specificity, sensitivity, precision, recall, F-score) for performance evaluation.

Main Results:

  • The ANN model achieved the highest accuracy: 97.50% on the PH2 dataset and 98.35% on the ISIC 2018 dataset.
  • Both proposed systems demonstrated superior performance compared to state-of-the-art methods on the tested datasets.
  • The feature-based ANN approach showed slightly higher accuracy than the CNN model in this study.

Conclusions:

  • The developed systems, particularly the ANN model, show high efficacy in the classification and detection of melanoma.
  • The study highlights the potential of integrating advanced AI techniques for improved skin cancer diagnostics.
  • Further evaluation and comparison of these systems confirm their utility in clinical settings for melanoma detection.