<![CDATA[Covid-19 telah mewabah sejak awal Tahun 2020. Meskipun terjadi penurunan jumlah kasus penderita Covid-19, namun masih terdapat beberapa kasus baru karena terjadi mutasi virus. Selain Covid-19, prevalensi pneumonia juga masih tinggi. Oleh karena itu, perlu dilakukan klasifikasi Covid-19 dengan pneumonia meskipun pascapandemi. Salah satu cara yang digunakan untuk mendeteksi Covid-19 dan pneumonia adalah menggunakan citra sinar-X dada. Pada studi ini dilakukan tidak hanya Covid-19 dan pneumonia, tetapi juga lung opacity dan normal. Beberapa tahun terakhir, marak digunakan pendekatan klasifikasi berbasis kecerdasan buatan. Beberapa studi telah dilakukan dengan menggunakan pendekatan deep learning berbasis arsitektur CNN. Pada studi ini, klasifikasi keempat kelas di atas dilakukan dengan menggunakan data yang lebih banyak, yaitu 21.165 citra sinar-X dada. Selain itu, dilakukan perbandingan kinerja sembilan arsitektur CNN, yaitu Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2, dan ResNet152v2. Sebagai tambahan, studi ini juga membandingkan kinerja dua pengoptimasi, yaitu Adam dan SGD untuk masing-masing arsitektur CNN. Kinerja tertinggi diperoleh dengan menggunakan arsitektur CNN berbasis ResNet50v1 dan pengoptimasi Adam dengan nilai rerata akurasi pelatihan, validasi, dan pengujian mencapai 92,22 ± 0,25 %. AbstractCovid-19 has been a global pandemic since the beginning of 2020. Although there has been a decrease in the number of cases of Covid-19, however, there are still some new cases due to virus mutation. Besides Covid-19, the prevalence of pneumonia is still high. Therefore, it is necessary to classify Covid-19 and pneumonia not only during a pandemic but also post-pandemic. One of the methods is using chest X-ray images. In this study, not only Covid-19 and pneumonia but also lung opacity and normal were carried out. In recent years, artificial intelligence-based classification approaches have been widely used. Several studies have been conducted using a deep learning approach based on Convolutional Neural Networks (CNN) architecture. This study aims to classify the four classes using 21,165 chest X-ray images. In addition, a comparison of the performance of nine CNN architectures was perfomed, i.e. Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2 and ResNet152v2. In addition, this study also compares the performance of two optimizers, i.e. Adam and Stochastic Gradient Descent (SGD) for each CNN architecture. The highest performance was obtained using ResNet50v1 and Adam optimizer with the average value of training, validation, and testing accuracy of 92.22 ± 0.25 %.]]>
Copyrights © 2022
