Model Prediktif Klasifikasi Tahap Katarak dari Citra Lensa Menggunakan MobileNetV2
DOI:
https://doi.org/10.29407/ka5czf90Keywords:
Deep Learning, Katarak, MobileNetV2, Streamlit, KlasifikasiAbstract
Katarak adalah penyebab utama kebutaan yang memerlukan klasifikasi akurat untuk menentukan waktu bedah yang optimal, namun metode diagnosis klinis seringkali terbatas di layanan kesehatan primer. Penelitian ini berfokus pada pengembangan dan validasi model prediktif berbasis MobileNetV2 untuk klasifikasi tiga-tahap katarak (Penglihatan Normal, Katarak Imatur, dan Katarak Matur) dari citra lensa. MobileNetV2 dipilih karena arsitekturnya yang lightweight dan efisien, menjadikannya ideal untuk implementasi pada lingkungan web yang membutuhkan respons cepat, menggunakan framework Streamlit. Proses image enhancement (seperti CLAHE) diterapkan untuk menajamkan kontras opasitas katarak sebagai pra-pemrosesan. Model dilatih menggunakan Transfer Learning dari bobot ImageNet. Evaluasi ditekankan pada metrik multi-kelas: Akurasi, Presisi, Recall, dan F1-Score (Macro Average). Hasil yang diharapkan akan menunjukkan bahwa MobileNetV2 mencapai akurasi klasifikasi yang tinggi mencapai 97,73% sambil mempertahankan efisiensi komputasi, menjadikannya alat skrining yang cepat, objektif, dan dapat diakses melalui browser web yang dapat mengurangi beban diagnostik di fasilitas kesehatan terbatas.
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