# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 2 Related Works  
This section contains research-related material, such as general work on the classification of lung disease data using the ResNet-50, ResNet-101, and ResNet-152 algorithms. These studies form the foundation for our investigation of efficient and effective methods for analyzing lung disorders using classification techniques. Various researchers have contributed to the identification of lung disease using artificial intelligence and X-ray imaging of the lungs as a result of rapid technological advancements. Medical imaging has become an essential diagnostic tool for a wide range of diseases since the discovery of X-ray imaging in 1895. [PERSON] was the first to discover X-rays in 1895 [13]. X-rays are a technique for diagnosing health problems and monitoring a person's health. A two-dimensional image of the inside of the human body is obtained by using long-wave electromagnetic radiation that can penetrate body tissue. Many imaging techniques have been developed since then, including computed tomography, magnetic resonance imaging, ultrasound, and positron emission tomography. The use of medical images for treatment dates back to the discovery of X-rays, and automatic analysis of medical images first appeared in 1960 [14]. Some research is conducted using computers as a tool, but due to limited access, this work is severely limited. Several researchers from the University of Chicago's Kurt Rossmann Laboratory began developing machine learning systems and medical image analysis techniques in the 1980s [15]. Some disease cases are extremely complex because they necessitate large amounts of data from various image types, such as 2D, which necessitates hundreds of images, and 3D, which necessitates thousands of images. As a result, as the amount of data analyzed grows, so does the workload of radiologists and physicians. Deep learning methods have become an important solution in medical imaging in recent years. CXR images are an example of medical data in the form of complex pattern images. As a result, a deep learning model is used in this study, with the goal of enabling computers to learn features automatically from data and perform complex tasks without being explicitly programmed. Several studies have shown that network depth is critical because it provides better accuracy results than shallower networks [16; 17]. Deeper networks can also introduce new issues like disappearing or exploding gradients, both of which have an adverse effect on the model, making the model more complex and challenging to drill [18]. He et al. [11] proposed a deep residual learning framework to address this issue. ResNet is a network that has deeper layers than the VGG network. The ResNet architecture introduces skip connections, also known as residual connections, which allow the flow of the gradients directly through alternative bypass paths [19]. This addresses the issue of vanishing gradients in deep neural networks, where gradients shrink to zero and impede learning. By using residual blocks, we can train an effective deep neural network. Through residual connections between layers, input can spread more quickly. As a result, we can expand our network, and ResNet offers researchers flexibility in determining the number of layers to use because it has several versions with different number of layers, such as ResNet-18 with 18 layers, ResNet-34 with 34 layers, ResNet-50 with 50 layers, and several other types of ResNet. However, it should be kept in mind that as the number of layers increases, so does the number of networks and parameters used. An experiment was first built in a study conducted by [PERSON] et al. [11] comparing plain networks with 18 and 34 layers. According to the results of these experiments, 34-layer plain nets have a higher error rate than 18-layer plain nets. However, when residual blocks and skip connections were added to the 34-layer plain net and the 18-layer plain net to create ResNet-18 and ResNet-34, the results changed, indicating that networks with more layers have higher error rates.