Performance Analysis of Skin Cancer Multi-Class Dataset Classification Method Using KNN and HOG
DOI:
https://doi.org/10.36085/jsai.v7i2.6423Abstract
Detection of skin cancer in its early phase is a challenge even for dermatologists. This study aims to analyze the performance of classification methods on multiclass skin cancer datasets using K-nearest neighbor (KNN) and histogram of oriented gradients (HOG). The dataset is taken publicly under the name Skin Cancer MNIST dataset: HAM10000 dataset totaling 10,015 data. The first experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The second experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The last experiment using the pixels per cell parameter of 8.8 and cells per block of 2.2 got the best accuracy of 61.43%.
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Copyright (c) 2024 Sarwati Rahayu, Sulis Sandiwarno, Erwin Dwika Putra, Marissa Utami, Hadiguna Setiawan
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.