Pengembangan Model Hybrid untuk Identifikasi Tuberkulosis Pada Gambar Rontgen Dada

Ridwan Mahenra

doi:10.58602/chain.v3i1.166

Ridwan Mahenra Universitas Teknokrat Indonesia

DOI: https://doi.org/10.58602/chain.v3i1.166

Keywords: Tuberkulosis, Model VGG-16, Pembelajaran mendalam, Jaringan Syaraf Tiruan, Sinar-X

Abstract

Tuberkulosis (TB), penyakit menular parah yang berdampak pada jutaan orang di seluruh dunia, umumnya didiagnosis dengan menggunakan rontgen dada. Untuk memastikan diagnosis yang akurat, terutama pada tahap awal, para profesional di bidang kesehatan mengandalkan dukungan teknologi canggih. Tidak seperti model yang sudah ada yang terutama berfokus pada deteksi TB pada gambar sinar-X, penelitian ini bertujuan untuk mengklasifikasikan gambar yang berhubungan dengan TB untuk memfasilitasi pemilihan metode yang tepat untuk deteksi TB yang tepat. Pendekatan yang diusulkan menggabungkan kemampuan yang kuat dari arsitektur VGG16 dengan jaringan syaraf tiruan (CNN) untuk tujuan klasifikasi. Memanfaatkan keefektifan VGG16 dalam menangkap fitur gambar yang penting, kami memodifikasinya untuk ekstraksi fitur untuk mengidentifikasi tanda-tanda TB pada gambar sinar-X. Untuk klasifikasi, CNN digunakan untuk mengkategorikan gambar yang terkena TB. Metode yang diusulkan ini dievaluasi menggunakan dataset standar, yang menunjukkan kinerja yang unggul dalam hal akurasi, recall, dan presisi dibandingkan dengan teknik yang ada saat ini.

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