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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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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features.

Syeda Shamaila Zareen1, Sun Guangmin1, Yu Li1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

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This study highlights the effectiveness of machine vision (MV) for identifying five skin cancer types. The multilayer perception (MLP) model achieved 97.13% accuracy using optimized texture features from ISIC datasets.

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Area of Science:

  • Dermatology
  • Computer Vision
  • Medical Imaging

Background:

  • Accurate identification of skin cancers is crucial for effective treatment.
  • Traditional methods can be subjective and time-consuming.
  • Machine vision offers a potential automated solution for skin lesion analysis.

Purpose of the Study:

  • To evaluate the efficacy of a machine vision (MV) approach for classifying five types of skin lesions: actinic keratosis, benign, solar lentigo, malignant, and nevus.
  • To develop and optimize a feature extraction and selection pipeline for skin cancer images.
  • To compare the performance of various classification algorithms on the extracted features.

Main Methods:

  • Utilized 1000 skin cancer images from the International Skin Imaging Collaboration (ISIC) dataset.
  • Extracted texture features using first-order histogram and Gray Level Co-occurrence Matrix (GLCM).
  • Applied Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) for dimensionality reduction, optimizing to 12 features.
  • Classified lesions using Naive Bayes (NB), Bayes Net (BN), LMT Tree, and Multilayer Perceptron (MLP) with 10-fold cross-validation.

Main Results:

  • Reduced 137,400 initial texture features to 12 optimized features.
  • The Multilayer Perceptron (MLP) classifier achieved the highest accuracy of 97.1333%.
  • The MV approach demonstrated significant potential in differentiating between various skin cancer types.

Conclusions:

  • Machine vision, particularly using MLP with optimized texture features, is a highly accurate method for skin cancer identification.
  • The developed feature extraction and selection methodology effectively enhances classification performance.
  • This automated approach could support dermatologists in early and precise skin cancer diagnosis.