Klasifikasi Covid-19 menggunakan Arsitektur DarkCovidNet pada Citra Radiografi X-ray Dada

Rima Tri Wahyuningrum, Wahyu Zainur Putra, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

Abstract


Covid-19 adalah penyakit severe acute respiratory syndrome. Coronavirus menjadi penyebab gangguan pernapasan dan infeksi paru paru, sehingga dapat menyebabkan kematian. Penyakit Covid-19 sudah tersebar ke seluruh negara termasuk negara Indonesia. Meskipun saat ini, Covid-19 telah mereda namun pencegahan maupun penanganannya tetap dibutuhkan. Oleh karena itu sangat diperlukan alat untuk mendiagnosis penyakit Covid-19 pada X-ray dada. Penggunaan klasifikasi citra berfungsi untuk memproses penggabungan piksel pada suatu citra ke dalam kelompok untuk diinterpretasikan sebagai bentuk properti yang spesifik. Dengan klasifikasi citra, mampu mempermudah pengelompokan individu untuk mewakili fitur kelas citra. Pada penelitian citra radiografi X-ray dada ini, menggunakan multiclass-classification yang terdiri dari 3 kelas yaitu: Covid-19, Normal (No-Findings), dan Pneumonia. Dataset yang diperoleh berjumlah 4.945 citra X-ray.  Pertama, dilakukan proses input citra dan resize image. Setelah itu dilakukan pembagian data yaitu 80% sebagai data train dan 20% sebagai data test. Pada proses pelatihan (train) akan menggunakan model DarkCovidNet. Arsitektur yang diusulkan terdiri dari 19 convolutional layer dan 5 maxpooling. Model ini terdapat proses DarkNet (DN). DN terdiri dari proses convolutional, batch normalization dan LeakyReLU. Pada skenario uji coba menggunakan optimasi Adam, reduce learning rate, dan menambahkan 3 hidden layer. Hasil uji coba terbaik terdapat pada uji coba keempat dengan hasil akurasi sebesar 95,85%, F1-score 95,89%, AUC 99,48%. Dengan demikian model DarkCovidNet tersebut sangat bagus dalam melakukan klasifikasi citra X-ray dada.


Keywords


Covid-19; Multiclass-Classifiation; Model DarkCovidNet; Akurasi; F1-Score

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References


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DOI: http://dx.doi.org/10.26418/jp.v10i1.69817

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