# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 2 Related Works
[30] investigated the use of ResNet-152 for Alzheimer's disease diagnosis. There was research conducted to classify binary classes and multi-classes. It was divided into two classes: Alzheimer's Disease and Cognitive Normal in binary classification, and Alzheimer's Disease, Cognitive Normal, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment in multiclass classification. ResNet-152 was tested with a variety of medical image datasets and was found to be 99.30% accurate for binary classification and 98.79% accurate for multiclass classification. Data processing was performed with great care, and it played an important role in improving accuracy. The authors contributed to the advancement of medical image-based diagnostic approaches for Alzheimer's disease by leveraging the power of ResNet-152. Previous research has shown that ResNet-50, -101, and -152 have advantages for accurate medical image analysis. When compared to the previously produced ResNet-34, the resulting ResNet-50, -101, and -152 have a higher level of accuracy [31]. This is supported by experimental results for error rates of various methods, which show that ResNet-50, -101, and -152 have lower error rate values than other methods such as VGG, GoogleLeNet, VGG (v5), PreLU-net, BN-inception, ResNet-34 B, and ResNet-34 C. ResNet-50 has a 5.25% top five error rate, ResNet-101 has a 4.60% top five error rate, and ResNet-152 has a 4.49% top five validation error. This single model outperforms all previous ensemble results. Experiments on the ImageNet dataset were also conducted, and it was discovered that ResNet-152 had a top five error of 3.57% in the test set, earning it first place at ILSVRC 2015. [PERSON] et al. [32] attempted to compare these ResNet-50, -101, and -152 architectures to classify data on human skin burns, which are divided into two categories: burnt skin and injured skin (bruises and pressure sores). Before beginning the classification stage, data were preprocessed by rotating and flipping. Two experiments were carried out during the feature extraction stage, namely by performing additional linear SVM to classify features that did not require additions. Based on these experiments, it was discovered that the ResNet-152 model, whether SVM was added or not, had the highest level of accuracy, namely 99.52% without SVM additions and 99.96% with SVM additions. After ResNet-152, ResNet-101 had the second highest level of accuracy, with 98.81% for those without additions and 99.87% for those with SVM additions. ResNet-50 had the lowest level of accuracy in this study when compared to the other two ResNets, with 98.33% for the model that was not added and 99.87% for the model that was added. This paper also discussed the discovery that increasing the number of layers in ResNet improves its accuracy. [PERSON] [33] investigated the impact of varying image dimensions and exposure fusion framework enhancements on the performance of pneumonia detection using different ResNet architectures, including ResNet-50, ResNet-101, and ResNet-152, in contrast to [PERSON] et al. This study discovered that ResNet-50 outperforms the other version of ResNet by systematically analyzing the influence of image dimension and exposure fusion techniques. Three ResNet architectures, ResNet-50, -101, and -152, are compared in this study. This ResNet series was chosen based on the research summary, which stated that ResNet-50, -101, and -152 are effective for medical image analysis. This study aims to prove that increasing the layer on the ResNet has an effect on the resulting level of accuracy and investigates which of the three architectures is best used in classifying lung disease from CXR.