Comparative Analysis of Skin Cancer Image with Classification and Clustering Algorithms

Authors

  • Muhammed Kara Ondokuz Mayıs University, Atakum,Samsun,55270,Turkey
  • Yüksel Terzi Ondokuz Mayıs University, Atakum,Samsun,55270,Turkey
  • Mehmet Ali Cengiz Ondokuz Mayıs University, Atakum,Samsun,55270,Turkey

Keywords:

Data science, machine learning, orange image processing, statistics

Abstract

Skin cancer is one of the most common and potentially life-threatening diseases worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. In recent years, the integration of advanced technologies, such as artificial intelligence and image analysis, has revolutionized the field of dermatology. This article presents a comprehensive comparative analysis of algorithms for classifying and clustering skin cancer images. The goal is to improve the accuracy and efficiency of skin cancer diagnosis.

The study explores various machine learning algorithms used for skin cancer image classification, such as support vector machines (SVM), decision trees, and k-nearest neighbors (KNN). These algorithms are evaluated based on their capacity to distinguish between benign and malignant skin lesions, with a particular emphasis on sensitivity, specificity, and accuracy. Apart from classification, clustering algorithms are also examined to determine their potential in grouping similar skin lesions. This can assist dermatologists in identifying patterns and anomalies within extensive datasets. K-means, hierarchical clustering, and DBSCAN are among the algorithms assessed for their effectiveness in organizing images of skin cancer.

The comparative analysis in this article aims to provide insights into the strengths and weaknesses of various algorithms, their computational efficiency, and their performance on diverse datasets. Furthermore, it explores the potential of combining classification and clustering techniques to develop a skin cancer diagnosis system that is more robust and accurate.

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Published

2023-12-24

How to Cite

Muhammed Kara, Yüksel Terzi, & Mehmet Ali Cengiz. (2023). Comparative Analysis of Skin Cancer Image with Classification and Clustering Algorithms. International Journal of Computer (IJC), 49(1), 229–244. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2157

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Articles