Implementasi Transfer Learning menggunakan Model Efficientnet-B3 pada Klasifikasi Pigmen Kanker Kulit
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Abstract
Kanker kulit memiliki beberapa faktor penyebab seperti faktor genetik hingga radiasi ultraviolet berlebihan dari paparan matahari. Meskipun diagnosis kanker kulit bisa dilakukan sejak dini, masyarakat seringkali enggan atau menganggap remeh untuk berkonsultasi dengan ahli, yang bisa menyebabkan kondisinya semakin parah. Teknik umum dalam diagnosa kanker kulit berupa biopsi, tetapi prosesnya sering kali rumit dan memerlukan biaya yang tinggi. Menurut Menurut World Health Organization (WHO), kanker merupakan faktor terbesar yang penyebab dari kematian di dunia, pada tahun 2020 data mencapai 10 juta kematian. Dalam era teknologi yang semakin maju, kanker kulit bisa dideteksi dengan menggunakan deep learning melalui penerapan metode Convolutional Neural Network (CNN) menerapkan model transfer learning EfficientNet-B3 pada klasifikasi pigmen kanker kulit. Penggunaan dataset pada penelitian ini adalah International Skin Imaging Collaboration (ISIC) 2018, yang terdiri dari 10015 citra dermoskopi kulit. Proses augmentasi data dan preprocessing melibatkan pengubahan ukuran citra menjadi 75 x 100 piksel serta penerapan metode hair removal preprocessing. Penelitian ini berfokus pada klasifikasi dua kelas, yaitu Benign (jinak) dan Malignant (ganas). Hasil eksperimen menunjukkan bahwa penggunaan model EfficientNet-B3 berhasil meningkatkan akurasi menjadi 86%, dengan hasil precision, recall, dan F1-score berturut-turut yaitu 0.86, 0.92, dan 0.89 ketika metode hair removal preprocessing diterapkan
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