# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 1 Introduction
One of the methods most frequently used to assess a patient's physical condition is an X-ray. A chest X-ray (CXR) examination is performed to diagnose lung disease, the results of which show the condition of the chest cavity, respiratory tract, heart, and lungs. The presence of unusual findings, or what are typically referred to as abnormal situations, in a radiological examination of a person's lungs performed by a radiologist defines the examination on their condition as being conducted. CXRs are employed to find anomalies in the cavity. Ground-glass opacity (GGO) is one of the anomalous phenomena that is identified [9]. Normal lungs have black CXR findings, but if the CXR findings show a gray area or an area covered with a membrane, the condition is ground-glass opacity. Most people with moderate to severe respiratory conditions show ground-glass opacity, which suggests an infection or inflammation brought on by viruses, fungi, bacteria, or parasites like pneumonia and COVID-19 [10]. Chest X-ray diagnosis and classification of lung diseases is a challenging and intricate task. In order to obtain accurate identification results, extra precision is required. To assist radiology specialists and accelerate the pace of implementation, it is necessary to collaborate with increasingly sophisticated technological advances, such as deep learning models. A convolutional neural network is a deep learning algorithm that uses mathematical operations in network learning, specifically convolution. This is a neural network algorithm that is commonly used for image data. The Residual Neural Network, also known as ResNet, is a popular CNN architecture. [PERSON] et al. [11] first proposed this architecture to ease the training of networks that are much deeper than those previously employed, as training becomes more difficult with deeper neural networks. The layers were reformulated as residual learning functions, also referred to as residual blocks. These building blocks, which are present in every layer of the ResNet CNN architecture, form its framework. Utilizing these residual blocks makesnetwork optimization simpler and allows for significantly better accuracy as depth increases. Furthermore, residual blocks can be used to solve the vanishing gradient problem [12], which occurs when the gradient information that the model is supposed to learn is unable to reach the first layer due to repeated multiplications. As a result, the first layer receives no gradient, rendering the CNN model unable to learn from the calculated errors. In CNN, various architectures, such as the ResNet architecture, have different variations despite originating from the same source. The only difference between these architectures is the number of layers and parameters, which are all the same. ResNet architectures have layers that correspond to their names; for example, ResNet-50 has 50 layers, ResNet-101 has 101 layers, and ResNet-152 has 152 layers. These layers perform various functions that help the machine learn the data and extract important information for the classification phase. The more layers used in an architecture, the deeper the learning process, and thus the better the performance. However, deeper architectures result in longer training times due to the increased number of parameters. As a result, the purpose of this study is to compare the ResNet-50, ResNet-101, and ResNet-152 architectures using CXR image data to classify pneumonia and COVID-19 diseases. The contributions of this research are as follows:  
1. Our study is useful for conducting a comparative analysis of the three ResNet architectures used, namely ResNet-50, ResNet-101, and ResNet-152, to identify diseases that affect the lungs using X-ray images. By obtaining the highest evaluation score and the lowest error rate for each architecture, it is possible to determine which ResNet architecture has the most optimal modeling, allowing accurate lung disease detection. 2.