# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 5 Discussion  
In this study, three different ResNet architectures were employed for the classification of four distinct class categories: COVID-19, pneumonia, lung opacity, and normal. The CXR data used in this research were sourced from [PERSON] et al. (2018) and [PERSON], [PERSON] et al. (2019) updates. Specifically, the study focused solely on images of normal lungs, pneumonia, lung opacities, and COVID-19 patient lungs, omitting consideration of other lung-related conditions such as fractures, pneumothorax, or heart failure. The third model was trained using a dataset comprising 16,933 images, which included 2893 COVID-19 images, 1076 pneumonia images, 4810 lung opacification images, and 8730 normal lung images. To ensure model performance and prevent overfitting, model testing was carried out using a test dataset. It is worth noting that this dataset only included four class classifications, but there is hope that additional class categories will be introduced in the future. This expansion would enable the model to identify a wider range of lung diseases comprehensively, thereby enhancing its utility for medical professionals in diagnosing various lung conditions with greater specificity. In this research, we used three ResNet architectures with different levels of depth. These architectures were ResNet-50, ResNet-101, and ResNet-152. The depth of each architecture corresponded to its respective name. ResNet-50 consisted of one convolution layer \(7\times 7\), followed by maxpooling \(3\times 3\), then entered the residual block section with skip connections, each consisting of three convolution layers of size \(1\times 1\) and \(3\times 3\), three for the first part, four for the second part, six for the third part, and three for the fourth part. Therefore, the total layers used in this architecture were 50 layers and produced an f-1 score of 90.75. The second architecture, namely ResNet-101, had the addition of a convolution layer from the ResNet-50 architecture, namely in the third part, the residual block and skip connection, so that three convolution layers measuring \(1\times 1\) and \(3\times 3\) became 23 pairs. This had an impact on the resulting F1-score value of 93.25. Likewise, for the third architecture, namely ResNet-152, which was a development of the previous ResNet architecture because it increased the number of convolution layers in the second and third residual blocks and skip connections to 8 pairs and 36 pairs, respectively. As with ResNet-101, which experienced an increase in the resulting F1-score value, ResNet-152 produced the highest F1-score value among the other ResNet architectures used, namely 94.50. This experiment illustrates that adding the number of layers from ResNet-50 to ResNet-101 can yield an improvement in model performance of up to 2.5%. A similar result was achieved when extending ResNet-101 by adding layers to ResNet-152, which had an increasing performance of 1.5%. The three architectures were used to create an X-ray image classification model with high evaluation values. The primary goal of CNN modeling is to achieve maximum accuracy with the least amount of loss. The assessment matrix found that each model had limitations in identifying lung opacity and normal classes due to a very large error value. According to the Centers for Disease Control and Prevention (CDC) (2018), this challenge can arise due to the presence of two distinct types of lung opacity: small opacity and large opacity. Small opacity refers to opacities with dimensions less than 10 mm, while