MobilenetV2 Architecture To Detect Covid-19 X-Ray Imagery
DOI:
https://doi.org/10.31764/justek.v5i2.11820Keywords:
Covid-19, Chest x-ray, MobileNetv2Abstract
Abstract: Â The COVID-19 pandemic has hit all over the world, in the last two years and has changed the pace, structure and nature of social life. This study aims to detect COVID-19 using a chest x-ray image dataset sourced from kaggle.com, which is divided into 4 categories. The proposed method is CNN with MobileNetV2 architecture, by dividing 80% train data and 20% test data into 224x224 and batch size 32. The optimizer uses SGD, lr 0.005, momentum 0.9 and epoch 20. The results of the study with the achievement of precision values for the covid category 0.99, lung opacity 0.98, normal 0.96 and viral pneumonia category reached 0.99. Further studies can use the development of the CNN model and can try with other optimizers.
Abstrak: Pandemi covid-19 telah melanda diseluruh dunia, dalam dua tahun terakhir dan mengubah langkah, struktur dan sifat kehidupan bermasyarakat. Penelitian ini  bertujuan untuk mendeteksi covid-19 menggunakan dataset citra chest x-ray yang bersumber dari kaggle.com, yang dibagi menjadi 4 kategori. Metode yang diusulkan yaitu CNN dengan arsitektur MobileNetV2, dengan membagi data train 80% dan data test 20% ukuran citra menjadi 224x224 dan batch size 32. Optimizer menggunakan SGD, lr 0.005, momentum 0.9 serta epoch 20. Hasil penelitian ini dengan capaian nilai presisi untuk kategori covid 0.99, lung opacity 0.98, normal 0.96 dan kategori viral pneumonia mencapai 0.99. Studi selanjutnya dapat menggunakan pengembangan dari model CNN serta dapat mencoba dengan optimizer yang lain.References
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