The Effect of Image Size on the Deep Learning Approach for Classifying Covid-19 Pneumonia - Budi Nugroho; Anny Yuniarti (Technium: Romanian Journal of Applied Sciences and Technology, 2023)
The scope of this study is related to the classification of covid-19 pneumonia which uses X-ray images of the lungs as input and is then classified into 3 categories, namely normal (healthy), pneumonia, and covid-19 pneumonia. The classification process uses a deep learning approach, which is becoming a trend nowadays because its performance is better than conventional machine learning approaches. The image dataset in this study is a collection of X-ray images of the lungs consisting of 1,525 normal images, 1,525 pneumonia images, and 1,525 Covid-19 pneumonia images. These images vary in size, but all images are larger than 1000x1000 pixels. The large size of the image makes the training time long and the computational resources required are large. Deep learning machines need to reduce the size of the image to make the process more effective. However, changes in image size may affect classification performance. Therefore, we conducted experiments in this study to determine the effect of image size changes on the classification performance of COVID-19 pneumonia using a deep learning approach. We use the Convolutional Neural Network method, which uses 22 layers with 5 convolution layers, for classification. The experimental results show that the size of the input image affects the classification performance of covid-19 pneumonia. This study provides important information that larger image sizes do not always result in better classification performance. In addition, the image size that is too small causes very low classification performance. The performance of the classification of covid-19 pneumonia using the CNN method shows the most optimal results at an input image size of 500x500 pixels.