CNN Architecture Comparison for Covid-19 Image Classification Process

Covid-19 detection is the most important stage in the process of diagnosing suspected Covid-19 patients. One of the detections is through lung X-ray images. However, currently, we need an algorithm that can directly detect lung X-ray images that have high accuracy rather than manual detection which has uncertain accuracy. Deep Learning Model using CNN is one way to create the algorithm. In CNN, many architectures can be used, but not all architectures are compatible with the data they have. In this study, we will compare the architectural capabilities of ResNet50, DenseNet121, InceptionV3, VGG16, and MobileNetV2 using 3000 X-ray image data. These research results are that MobileNetV2 gets the highest accuracy value, which is 0.96. Then followed by VGG16 with an accuracy of 0.95, then InceptionV3 with an accuracy of 0.92, followed by DenseNet121 with an accuracy of 0.89, and finally, ResNet50 with an accuracy of 0.86. In the experiment, it was found that the architecture that has a larger number of layers has a lower accuracy value and a higher loss value than the architecture that has a smaller number of layers.