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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Sofy, Annisa Kurnia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Image Segmentation Using Hybrid Clustering Algorithms for Machine Learning-Based Skin Cancer Identification Maulana, Riza; Interiesta, Diva Cahaya; Sofy, Annisa Kurnia; Maulana, Ilham Habib; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.21016

Abstract

Early identification of skin cancer is crucial to increasing the chances of a cure and reducing mortality rates. This research aims to develop a method for identifying skin cancer using image processing techniques, specifically the hybrid clustering method. This method integrates machine learning with fuzzy c-means clustering (FCM) and hierarchical clustering (HC) segmentation techniques to segment skin cancer more accurately. Hybrid clustering is used to separate suspicious areas in skin images, resulting in more precise segmentation compared to conventional methods. The segmentation results are then used as input for various machine learning methods that are trained to recognize patterns in identifying types of skin cancer. Tests were carried out using data obtained from the Kaggle Dataset, and the results showed that the proposed method was able to achieve a high level of accuracy in identifying skin cancer. After segmentation, the ensemble learning method yielded the best identification results. The Random Forest algorithm, which is applied to process and analyze features from skin images, shows higher performance compared to other machine learning methods. Tests show that the Random Forest method with the proposed segmentation achieves an accuracy level of up to 89%, while other machine learning methods such as K-Nearest Neighbor only achieve an accuracy level of around 86%. This research makes an important contribution to the development of efficient and reliable diagnostic tools for skin cancer identification, with appropriate segmentation methods proven to increase accuracy.
Co-Authors Amir Saleh Interiesta, Diva Cahaya Maulana, Ilham Habib MAULANA, RIZA