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Skin lesion classification system using a K-nearest neighbor algorithm.

Mustafa Qays Hatem1

  • 1Renewable Energy Department, Technical Institute of Baqubah, Middle Technical University, Dyala, 32001, Iraq. mq.mu7@yahoo.com.

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|March 1, 2022
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Summary

This study introduces a MATLAB system using K-nearest neighbor (KNN) for accurate skin lesion classification. The machine learning approach achieved 98% accuracy in distinguishing normal from malignant skin lesions.

Keywords:
Graphical user interfaceK-nearest neighborMATLABMachine learningSkin detectionSkin disease

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate disease diagnosis is critical in healthcare, especially in dermatology, which presents diagnostic challenges.
  • Current machine learning diagnostic systems often lack sufficient accuracy.
  • There is a need for rapid and reliable diagnostic methods in dermatology.

Purpose of the Study:

  • To develop a MATLAB-based system for identifying and classifying skin lesions.
  • To improve the accuracy and efficiency of skin lesion diagnosis using machine learning.

Main Methods:

  • Implementation of the K-nearest neighbor (KNN) algorithm for classification.
  • Development of a system in MATLAB for image analysis and lesion identification.
  • Utilizing KNN for its time efficiency and high accuracy potential.

Main Results:

  • The developed system achieved a 98% accuracy rate in classifying skin lesions.
  • Successfully differentiated between normal and malignant skin lesions.
  • Demonstrated the effectiveness of the KNN approach in this context.

Conclusions:

  • The proposed MATLAB system with KNN offers a highly accurate and efficient method for skin lesion classification.
  • This approach can aid dermatologists in making faster and more reliable diagnoses.
  • Machine learning, specifically KNN, shows significant promise for advancing dermatological diagnostics.