## 2 Literature Review
The work in [36] proposes an architecture that uses standard LeNet model that is deployed for three different types of images in order to classify lung disease. These images are computer tomography (CT) scans, positron emission tomography (PET) scans, and a combination of CT and PET scans. An accuracy of 99.33% is observed via the use of such a multimodal architecture for classification. Figure 4: Plnfection net (InfNet) architecture specialized for disease detection [28]. Data from this highly accurate classification model can be used for post processing in order to identify progressive lung disease spreading. The work in [37] proposes use of classification and regression tree and iterative dichotomiser 3 (ID3) classifiers in order to identify progression of lung diseases. This work combines image processing with physiological parameters such as smoking history, number of hospitalizations, pulse rate, systolic & diastolic pressure, pulmonary heart disease, bronchiectasis, hypertension, coronary heart disease, malignant tumor, viral hepatitis, temperature, respiratory rate, chronic kidney disease, hypertension, cerebrovascular disease, and cirrhosis in order to identify disease progression. An accuracy of 94% is obtained via the use of this model, which is high enough to be used for real-time purposes. This concept can be used with other systems for improving the effectiveness of their application in lung disease detection. For instance, the work in [38; 39; 40] can use physiological parameters to identify present lung condition, and then correlate this data with chest X-ray scans in order to improve the efficiency for classification of COVID-19, Chronic Obstructive Pulmonary Disease, and lung cancer, respectively. Efficiency of systems can also be improved by modifying the manner in which chest X-ray scans are captured. The work in [41; 42; 43] identifies classification accuracy difference between deep-breathing and breath-holding scans. From this study, it is observed that breath holding scans showcase higher accuracy, and can be used for identification of any kind of respiratory diseases. Other novel methods like inter & intra-software reproducibility using lung density measurements, and elastography are defined in [44; 45; 46; 47; 48; 49]. These methods can be used for fine tuning accuracy measures for lung disease detection systems, and can be applied to both CNN and non-CNN architectures. Similar architectures are used in the underlying research, wherein breath holding with data augmentation are extensively utilized along with transfer learning in order to improve overall accuracy of lung disease detection. The next section demonstrates the proposed model based on transfer learning and deep nets. Gated Recurrent Unit (GRU) Based Transfer Learning Model with Deep Convolutional Neural Networks (DCNNs) for COPD Analysis  
The proposed model for COPD analysis uses an ensemble of VGGNet, AlexNet, ResNet18, and GoogLeNet CNN models. These models are selected based on the advantages and shortcomings in each of them. The VGGNet model is capable of high accuracy coarse classification, wherein images of different visual appearance can be classified with accuracies in the range of 95 to 98%. However, for minor changes in the images, the accuracy of VGGNet model reduces drastically. For instance, if lung nodule locations are widely separated as shown in Figure 5a, then VGGNet model works perfectly, but if the lung nodule locations for different disease types change infinitesimally as shown in Figure 5b, then the accuracy of the VGGNet model is not guaranteed. In order to remove this drawback, ResNet18 architecture of CNN is used, this architecture provides good accuracy for any kind of lung images, but this accuracy is saturated to 94 to 95% depending upon the dataset being used. In order to improve this accuracy further, the GoogLeNet model is fused with the ResNet18 model. This is done by replacing the convolutional layers of ResNet18 architecture with inception layers as suggested by the GoogLeNet architecture.