Perbandingan Kemampuan Klasifikasi Citra X-ray Paru-paru menggunakan Transfer Learning ResNet-50 dan VGG-16

Authors

  • Tasya Berliani Universitas Kristen Krida Wacana
  • Enggalwiguno Rahardja Universitas Kristen Krida Wacana
  • Lina Septiana Universitas Kristen Krida Wacana

DOI:

https://doi.org/10.28932/jmh.v5i2.6116

Keywords:

Covid-19, ; klasifikasi citra medis, x-ray dada, , ResNet-50, VGG-16

Abstract

Di masa pandemi Covid-19, foto rontgen menjadi umum digunakan untuk memeriksa pasien diduga Covid-19. Pada citra x-ray paru-paru yang terkena Covid-19 ditemukan adanya bercak putih atau flek. Namun, paru-paru yang memiliki flek ini tidak selalu disebabkan oleh Covid-19. Tujuan penelitian ini adalah untuk mengklasifikasikan beberapa jenis penyakit paru-paru dari citra x-ray, yaitu paru-paru dengan Covid-19, paru-paru dengan pneumonia, dan paru-paru yang memiliki flek dibandingkan dengan yang normal. Proses klasifikasi data pada penelitian ini dilakukan dengan membandingkan dua model yaitu CNN VGG-16 dan ResNet-50 dengan model yang telah dilatih sebelumnya. Metrik evaluasi yang digunakan dalam penelitian ini terdiri dari akurasi, presisi, sensitivitas, spesifisitas, skor F1, dan kecepatan waktu inferensi. Hasil menunjukkan bahwa VGG-16 lebih unggul dari ResNet-50 dalam hal kecepatan inferensi namun tidak dalam hal metrik evaluasi lainnya. Perubahan parameter juga menunjukkan hasil yang berbeda, epoch 200 adalah nilai optimal. Untuk mendapatkan hasil yang optimal diperlukan finetuning dengan menyesuaikan kondisi data yang digunakan. Sebagai simpulan, VGG-16 memiliki kemampuan klasifikasi yang lebih baik dibandingkan ResNet-50, namun perlu terus dikembangkan dengan memperbanyak data klinis yang aktual.

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Published

2023-08-31

How to Cite

1.
Berliani T, Rahardja E, Septiana L. Perbandingan Kemampuan Klasifikasi Citra X-ray Paru-paru menggunakan Transfer Learning ResNet-50 dan VGG-16. J. Med. Health [Internet]. 2023Aug.31 [cited 2024May18];5(2):123-35. Available from: https://journal.maranatha.edu/index.php/jmh/article/view/6116

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