# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 4 Results  
The preceding section outlines the data analysis procedure, including the stages of data preparation, research steps aligned with the selected approach, and model evaluation. In this chapter, we offer a comprehensive insight into the process and outcomes of classifying chest X-rays (CXR) using a Convolutional Neural Network (CNN) with the ResNet-50, ResNet-101, and ResNet-152 architectures. In this section, we delve into the training process of the three ResNet architectures, aiming to achieve precise classification of CXR images. Each incoming image serves as input and undergoes an initial convolutional layer with kernel sizes of 77% and 33%. The ResNet architecture is partitioned into four stages, commencing with the first stage, which comprises three residual blocks, each consisting of three layers. The convolutional operations within the layers of the first-stage blocks employ kernel sizes of 64, 64, and 128, respectively. The number of layers for each architecture differs in the subsequent stages. In the second stage, Res-Net-50 and ResNet-101 have four layers, whereas Res-Net-152 boasts eight. In terms of kernel sizes, the second stage employs sizes of 128, 128, and 512 for each architecture, respectively. Moving to the third stage, ResNet-50 employs six layers, ResNet-101 incorporates 23 layers, and ResNet-152 features an impressive 36 layers. Here, the kernel sizes are 256, 256, and 1024 for the respective architectures. In the fourth stage, all three architectures share the same number of layers. The kernel sizes applied in each architecture for this stage are 512, 512, and 2048, respectively. Furthermore, the network incorporates an average pooling layer, followed by a fully connected layer consisting of 1000 neurons that represent the ImageNet classes. A bottleneck design is used for deeper networks such as ResNet-50, ResNet-101, and ResNet-152. Three layers are stacked together for each residual function. These layers are made up of 11, 33, and 11 convolutions. The dimensions are reduced and then restored by the 1-1 convolution layer. With smaller input and output dimensions, the three-layer system acts as a bottleneck. Skip connections are performed for three layers in the ResNet architecture. The convolution operation in the residual block is performed with a stride size of two, reducing the input size to half of its height and width when transitioning from one stage to the next. Figure 2: The General Classification process using ResNet-50, ResNet-101, and ResNet-152. Following the training process, the model is evaluated to determine its performance. The model's performance is determined by making predictions on the test data and comparing the results to the true labels. The resulting confusion matrix is shown below. According to Table 4, the research findings do not indicate the occurrence of overfitting. The small difference in accuracy values between the training and testing processes demonstrates this. Similarly, there are no significant differences between the training and testing loss values. Table 5 compares the classification performance of the three ResNet architecture models on CXR images. To evaluate the performance of each model, we examine the values of each metric used, namely recall, precision, and F1-score. The ResNet-152 architecture achieves the highest performance in each metric by using a learning rate of 10-6. The ResNet-50 architecture, on the other hand, yields the model with the lowest performance in each metric. Table 6 provides a visual representation of the identification outcomes based on the different ResNet architectures utilized in this study. From the information presented, it is evident that the three ResNet architectures performed reasonably well in recognizing various lung diseases among patients. However, it is important to note that these models are not without their limitations.