## 2 Literature Review
Both the architectures (i.e., VGGNet and GoogLeNet powered ResNet18) might produce different results during classification, thus as a decision maker in case of different results, the AlexNet architecture is used. This architecture is able to produce accuracy values in the range of 90 to 95%, which is high enough for verification purposes. The overall architecture of the proposed CNN based classification model can be observed from Figure 6, wherein all the 4 CNN models are combined to obtain the final COPD status. The proposed architecture is effective for classifying dataset imagery taken from LUNA 16 and Kaggle Chest X-ray datasets. However, the performance reduces when real-time image data is used for classification. In order to improve the classification performance of the proposed CNN model, a GRU based transfer learning approach is  
* The feature extraction unit is a combination of bidirectional GRU with recurrent neural networks, and produces a feature vector as shown in Figure 7.
* All of these features are given to a feature mapping unit that uses Quadratic support vector machine (QSVM) in order to find out the best matching training set image for the given input features as shown from Figure 8.
* Features of this selected training set image are merged with the input image using a linear & deformable image registration engine. * The registration engine evaluates the input image (image from dataset) and the real time image (image to be classified), and passes them through linear registration model. * The output of linear registration model is given to deformable registration model, wherein a deformable field is evaluated. * Result of this field, is the deformable registered image, which can be further used for classification purposes as can be seen from Figure 9, where the entire registration process is shown. * This registered image is given to the proposed ensemble CNN Model for final classification. In order to perform final classification, the proposed classifier uses a combination of GoogLeNet and ResNet18 architectures as already described. The modified architecture of this model can be observed from Figure 10a, wherein all the internal convolutional layers  
Figure 8: Data augmented transfer learning model for real time COPD classification. Figure 7: Bidirectional GRU with RNN for feature extraction. of the ResNet18 architecture are replaced by the Inception Net layers from GoogLeNet. The inception layer can also be observed from Figure 10b, wherein inception V2 is used for effective classification. In order to enhance classification accuracy, inception module uses the following equation for internal pooling,  
\[P(q,p)=\sum\log(C(p,q)*B(q,p)) \tag{3}\]  
where '\(P\)' is the output of Pooling, 'C' is the convolutional operation on the image patch (\(p\), \(q\)), and '\(B\)' is the base image patch (\(q\), \(p\)). Figure 10: (**a**) Proposed GoogLeNet injected ResNet18 model for high accuracy classification. (**b**) Architecture of the inception module injected in ResNet18. Figure 9: Registration process. The result of this equation is given to a filter concatenation unit, which operates using the following Equation,  
Figure 3:0  
where '\(F\)' is the filtered output, '\(P\)' is the pooling output, '\(B\)' is the base image patch for (\(p\), \(q\)), while '\(a\)', '\(c\)', '\(d\)' and '\(k\)' are inception constants, which have the value between 0 to 255 depending upon the level of inception. The filtered output is then used by the ResNet18 architecture in order to obtain the final classification results. The GRU model was again used with aggregated COPD data and person's expected life expectancy was evaluated. This expectancy evaluation was done for people whose life expectancy values are known. In order to evaluate accuracy of the proposed model, the next section evaluates the model on LUNA 16, Kaggle Chest X-ray and a real-time custom dataset. The accuracy of classification is compared with different state-of-the-art models.