Deteksi Pneumonia Menggunakan Citra Sinar-X Paru berbasis Residual Network
DOI:
https://doi.org/10.25126/jtiik.2022925626Abstrak
Fokus dari studi ini adalah membandingkan kinerja Mask R-CNN dengan ResNet-50 & 101 sebagai backbone pada deteksi pneumonia menggunakan citra Sinar-X paru. Selain itu, juga dibandingkan pengaruh augmentasi data berupa rotasi dan refleksi serta pengaruh teknik perbaikan kualitas citra menggunakan CLAHE pada kinerja deteksi pneumonia. Set data yang digunakan pada studi ini berjumlah 26.684 citra Sinar-X paru yang diambil dari situs Kaggle RSNA Challenge. Terdapat empat parameter kinerja deteksi yang digunakan, yaitu akurasi; presisi; sensitivitas; dan spesifisitas. Meskipun ResNet-50 backbone yang memiliki 50 lapisan konvolusi, akan tetapi kinerja deteksi yang dihasilkan lebih baik dibandingkan dengan ResNet-101 yang memiliki 101 lapisan konvolusi. Selain itu, ResNet-50 backbone dengan augmentasi data berupa rotasi dan refleksi pada set data citra yang diperbaiki kualitasnya menggunakan CLAHE memberikan kinerja yang paling baik. Nilai akurasi yang diperoleh mencapai 76% dengan presisi 62%, spesifisitas 74% dan sensitifitas 67%. Pada studi ini, terlihat bahwa peningkatan jumlah lapisan konvolusi tidak berpengaruh terhadap peningkatan kinerja deteksi. Sebagai tambahan, kinerja deteksi pneumonia menggunakan Sinar-X paru berbasis ResNet-50 backbone dapat ditingkatkan dengan menambahkan prapemrosesan berupa augmentasi data serta perbaikan kualitas citra menggunakan CLAHE.
Abstract
The focus of this study is to compare the performance of the Mask R-CNN using ResNet-50 & 101 backbone on pneumonia detection using a chest X-ray image. In addition, the effect of data augmentation in the form of rotation and reflection was also compared. Furthermore, the effect of the image enhancement technique using CLAHE on pneumonia detection is performed. In total, 26,684 chest X-ray images are used. These images were downloaded from the Kaggle RSNA Challenge website. There are four parameters are used to evaluate the detection performance, i.e., accuracy; precision; sensitivity; and specificity. Although ResNet-50 backbone has 50 convolutional layers, the detection performance is better than ResNet-101 which has 101 convolution layers. In addition, the ResNet-50 backbone with data augmentation and image enhancement using CLAHE provides the best performance. The accuracy of 76%, the precision of 62%, specificity of 74%, and sensitivity of 67% are obtained using this proposed method. In this study, the increase in the number of convolutional layers has no effect on the detection performance. In addition, the performance of pneumonia detection using ResNet-50 backbone using chest X-rays image can be improved using data augmentation and image quality enhancement using CLAHE.
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