Breast Cancer Detection Using EfficientNetV2 Variants and Data Augmentation: A Comparative Study
DOI:
https://doi.org/10.23960/komputasi.v13i1.281Keywords:
Deteksi Dini, EfficientNetV2, Kanker Payudara, Citra MedisAbstract
Kanker merupakan penyebab utama kematian kedua di dunia yang menyebabkan sekitar 9,6 juta kematian. Deteksi dini kanker dan penanganannya sedini mungkin dapat menurunkan angka kematian. Metode deep learning terbukti mampu mengenali pola dalam citra medis dan memberikan hasil klasifikasi yang akurat, EfficientNet merupakan salah satu metode deep learning. Penelitian ini bertujuan untuk mengeksplorasi penggunaan berbagai varian EfficientNetV2, yaitu EfficientNetV2-S, EfficientNetV2-M, dan EfficientNetV2-L untuk mendeteksi kanker payudara berdasarkan citra medis. Dataset yang digunakan yakni gambar USG wanita berusia antara 25 dan 75 tahun sebanyak 600 wanita, dengan total 780 gambar USG yang memiliki ukuran rata- rata 500×500 piksel dalam format PNG. Setiap gambar dalam dataset ini diklasifikasikan ke dalam tiga label yaitu normal, jinak (benign), dan ganas (malignant). Penelitian ini mendapatkan hasil akurasi terbaik pada model EfficientNetV2-L dibandingkan model EfficientNetV2-S dan EfficientNetV2-M. Nilai akurasi pelatihan yang didapatkan sebesar 89% dan nilai akurasi validasi sebesar 86% dengan nilai loss pelatihan sebesar 0.30 dan nilai loss validasi sebesar 0.37. Berdasarkan hasil tersebut, penelitian ini memiliki potensi sebagai solusi pendukung keputusan medis yang efisien dalam praktik klinis sehari-hari untuk deteksi dini kanker payudara.
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