Klasifikasi Penyakit Paru-Paru Berdasarkan Peningkatan Kualitas Kontras dan EfficientNet Menggunakan Gambar X-Ray

Asfa Dhevi Azzumzumi; Muhammad Hanafi; Windha Mega Pradnya Dhuhita

doi:10.34148/teknika.v13i2.881

Asfa Dhevi Azzumzumi Program Studi Informatika, Universitas Amikom Yogyakarta, DI Yogyakarta
Muhammad Hanafi Program Studi Informatika, Universitas Amikom Yogyakarta, DI Yogyakarta
Windha Mega Pradnya Dhuhita Program Studi Informatika, Universitas Amikom Yogyakarta, DI Yogyakarta

DOI: https://doi.org/10.34148/teknika.v13i2.881

Keywords: Penyakit Paru-paru, CLAHE, EfficientNet, Covid-19, White Balance

Abstract

COVID-19 dan penyakit paru-paru telah menjadi faktor utama penyebab kematian manusia di seluruh dunia. Kematian pasien dipengaruhi oleh keterlambatan deteksi dini. Sebagian besar profesional medis menggunakan gambar untuk mengidentifikasi kondisi paru-paru. Namun, para ahli yang dapat me-diagnosis dengan gambar sangat terbatas. Diagnosis gambar mendiagnosa menggunakan penglihatan manusia secara konvensional. Klasifikasi penyakit paru-paru sangat bervariasi. Masalah yang disebutkan di atas menunjukkan bahwa deteksi penyakit paru-paru dengan Artificial Intelligence (AI) yang efektif telah ditetapkan. Namun, sebagian besar hasil penyakit paru-paru salah didiagnosis. Bagi pasien, masalah ini menjadi masalah besar. Bertujuan untuk menangani klasifikasi penyakit paru-paru dengan deteksi kesalahan yang tinggi, kami menggunakan beberapa teknik pre-processing gambar dan menerapkan model pembelajaran mendalam dalam EfficientNet. Model Pre-processing termasuk augmentasi, peningkatan white balance, dan peningkatan kontras. Berdasarkan penelitian sebelumnya, mayoritas proses analisa gambar medis mengalami kualitas gambar yang rendah. Berdasarkan laporan eksperimen, model yang kami usulkan mencapai hasil yang signifikan dalam mengurangi kesalahan deteksi pada klasifikasi penyakit paru-paru. Dimana hasil F1 score-nya 0,97, recallnya 0,98, presisinya 0,96, dan akurasinya 0,97. Kami mempertimbangkan untuk menggunakan model yang kami usulkan dalam klasifikasi multi-class. Kami mengevaluasi model yang kami usulkan menggunakan evaluation metric dan AUC Curve.

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