CNN-Based for Skin Cancer Classification with Dull Razor Filtering and SMOTE

KZ; Widhia Oktoeberza; (Scopus ID: 56669681900; Universitas Bengkulu); Prasetio; Wahyu Dwi; Universitas Bengkulu; Ramadhan; Adam Idham; Universitas Bengkulu; Putra; Adde Nanda C.; Universitas Bengkulu; Eliora; Firsti; Universitas Bengkulu; Mainil; Afdhal Kurniawan; Universitas Bengkulu; Susanto; Agus; Universitas Bengkulu

Telematika

Universitas Amikom Purwokerto

📄 Abstract

Skin cancer is usually diagnosed by dermatologists through biopsy, which can be a time-consuming process due to limited resources. Early detection of skin cancer can increase the survival rate to over 99%, but if it's detected late, the rate drops to around 14%. This finding highlights the need for a rapid and accurate computing system for early cancer detection, which can prevent severe consequences. The purpose of this study is to classify skin cancer images into benign and malignant classes based on their nature. To facilitate the classification of CNN-based skin cancer, this research employs dull razor filtering. Additionally, the SMOTE method handles the unbalanced dataset. The classification results indicate that the proposed approach has an accuracy of 88.54%, a precision of 88%, and a sensitivity of 88%. These findings suggest that CNN-based methods can aid dermatologists in the diagnosis of skin cancer.

🔖 Keywords

#CNN; Dull Razor Filtering; Early Detection; Skin Cancer; SMOTE

ℹ️ Informasi Publikasi

Tanggal Publikasi

05 March 2025

Volume / Nomor / Tahun

Volume 18, Nomor 1, Tahun 2025

📝 HOW TO CITE

KZ, Widhia Oktoeberza; (Scopus ID: 56669681900, Universitas Bengkulu); Prasetio, Wahyu Dwi; Universitas Bengkulu; Ramadhan, Adam Idham; Universitas Bengkulu; Putra, Adde Nanda C.; Universitas Bengkulu; Eliora, Firsti; Universitas Bengkulu; Mainil, Afdhal Kurniawan; Universitas Bengkulu; Susanto, Agus; Universitas Bengkulu; , "CNN-Based for Skin Cancer Classification with Dull Razor Filtering and SMOTE," Telematika, vol. 18, no. 1, Mar. 2025.

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