# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 2 Related Works
Then, in the following experiment, the authors changed the ResNet-34 layers by replacing every two-layer block with three-layer bottleneck blocks, yielding ResNet-50. ResNet-101 and ResNet-152 were created as a result of changes to more than three-layer blocks. Despite the fact that the addition reduced the number of FLOPs, ResNet-152 (11.3 billion FLOPs) had a lower complexity than VGG-16 and VGG-19 (15.3 and 19.6 billion FLOPs, respectively). [PERSON]'s 2023 research comparing ResNet-101 and VGG-19 to identify the severity of prostate cancer [20] found that the ResNet-101 architecture had an average accuracy value of 97.70%, while the VGG-19 architecture had an accuracy value of 96.49%. ResNet-101 outperformed VGG-19 in this case; additionally, ResNet-101 required 34 min and 38 s to train the model, whereas VGG required 56 min and 38 s. ResNet's advantage, with its large number of layers, is the reason why it is widely used in a variety of studies, including medical image data research. For example, [PERSON] and [PERSON] [21] developed a model to detect pneumonia in patients, allowing a more quick and accurate run of the detection process. The ResNet-50 architecture was used in conjunction with transfer learning to obtain images from two distinct classes, normal and pneumonia. The model was then evaluated using the confusion matrix, and the result was that it had a high accuracy value of 94%. ResNet-50 was used by [PERSON] and [PERSON] [22] to accurately diagnose breast cancer in histopathological images, demonstrating its potential for improving medical image interpretation. Similarly, [PERSON] and [PERSON] [23] used ResNet-50 and transfer learning to achieve proficient malaria cell image classification, demonstrating its utility in disease detection. Furthermore, [PERSON] et al. [24]addressed cancer detection using ML and the ResNet-50 architecture, extending the model's potential in the field of histopathology with promising results. The ResNet-101 architecture was also used to classify medical image data on brain tumor datasets [25]. The study included three types of tumors: meningiomas, gliomas, and pituitary tumors. The preprocessing process includes zero-centering and intensity normalization; additionally, the evaluation results are compared using augmentation in the preprocessing process. The accuracy value produced without augmentation was found to be 89.93%, but when additional augmentation was added to the data, the accuracy rate increased to 93.83%. The use of augmentation during the preprocessing stage was shown to improve model accuracy in [PERSON] et al. [26] studies. The use of the ResNet-101 architecture also demonstrated remarkable significance in a variety of domains of medical image analysis, as evidenced by the following studies. [PERSON] et al. [27] took on the difficult task of dealing with noisy labeled medical images, a common issue in real-world datasets, by using ResNet-101 for robust skin lesion classification. Their research demonstrated the architecture's ability to improve classification accuracy, particularly in scenarios with label noise. [PERSON] et al. [28] investigated COVID-19 classification in the context of the ongoing global health crisis using an innovative application of medical image synthesis using generative adversarial networks (GANs). Their research used ResNet-101 as a key component in generating and classifying synthetic medical images, contributing to the fight against the pandemic and demonstrating the adaptability of the ResNet-101 architecture. The ResNet-50 and ResNet-101 architectures proved to be versatile and effective tools for medical image analysis, as evidenced by the studies listed below. [PERSON] et al. [29] used ResNet-152, a ResNet variant, to extract features for MRI brain tumor classification. Their work demonstrated the architecture's ability to extract meaningful features from medical images, which were then optimized for dimension reduction to improve brain tumor classification accuracy. In a related field, [PERSON] et al.