## 4 Results and Statistical Comparison  
Evaluation of performance for the proposed model requires a large variety of input images to be used for training, testing, and validation purposes. To gather these images, the LUng Nodule Analysis (LUNA) 2016 dataset imagery is combined with Kaggle Chest X-ray dataset and real-time images taken from 'Modern Lab & X-ray', Pune. A total of 15,000 images were collected for this purpose, and each of these images are categorized into either COPD or non-COPD classes. Accuracy, precision, recall, and fMeasure values were compared for COPD detection for the proposed model and the models defined in [7, 19, 28]. The following Table 1 indicates the accuracy performance of the proposed model w.r.t. number of images used for testing. The image dataset was divided into 70:30, wherein 10.5k images were used for training the model, while remaining 4.5k images were used for testing the model and evaluating its performance. The following Table 1 showcases the test accuracy (TA) performance of all the models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Images** & **TA (\%)** & **TA (\%)** & **TA (\%)** & **TA (\%)** \\  & [7] & [19] & [28] & **Proposed** \\ \hline
100 & 79.50 & 79.85 & 79.98 & 91.50 \\
200 & 80.20 & 80.35 & 80.41 & 91.70 \\
300 & 80.50 & 80.60 & 80.65 & 91.90 \\
500 & 80.70 & 80.80 & 80.81 & 92.30 \\
700 & 80.90 & 80.83 & 80.85 & 92.50 \\
900 & 80.76 &From the test accuracy, it can be observed that the proposed model is highly efficient, and has a better performance when compared to state-of-the-art classification models. Average accuracy is around 8% higher when compared to these models, and can be observed from the following Figure 11, wherein these accuracy values are plotted. The following Table 2 showcases the test precision (TP) performance of all the models. From the test precision values, it can be observed that the proposed model is highly efficient and as better performance when compared to state-of-the-art classification models. Average precision is around 9% higher when compared to these models, and can be observed from the following Figure 12, wherein these precision values are plotted. The following Table 3 showcases the test recall (TR) performance of all the models. From the test recall, it can be observed that the proposed model is highly efficient and as better performance when compared to state-of-the-art classification models. Average  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Images**} & **TP (\%)** & **TP (\%)** & **TP (\%)** & **TP (\%)** \\  & [7] & [19] & [28] & **Proposed** \\ \hline
100 & 77.90 & 78.15 & 78.24 & 89.37 \\
200 & 78.39 & 78.51 & 78.57 & 89.56 \\
300 & 78.63 & 78.73 & 78.76 & 89.85 \\
500 & 78.83 & 78.84 & 78.86 & 90.15 \\
700 & 78.86 & 78.89 & 78.92 & 90.29 \\
900 & 78.92 & 79.02 & 79.06 & 90.39 \\
1000 & 79.12 & 79.19 & 79.23 & 90.54 \\
1200 & 79.27 & 79.33 & 79.83 & 90.73 \\
1500 & 79.39 & 81.33 & 82.40 & 90.98 \\
1800 & 83.27 & 85.63 & 86.37 & 91.32 \\
2000 & 87.99 & 88.58 & 88.89 & 91.56 \\
2250 & 89.16 & 89.84 & 90.15 & 91.95 \\
2500 & 90.52 & 91.08 & 91.38 & 93.42 \\
2800 & 91.64 & 92.29 & 92.59 & 95.15 \\
3000 &recall is around 6% higher when compared to these models, and can be observed from the following Figure 13, wherein these recall values are plotted. The following Table 4 showcases the test fMeasure (TF) performance of all the models. From the test fMeasure, it can be observed that the proposed model is highly efficient, and as better performance when compared to state-of-the-art classification models. Average fMeasure is around 8% higher when compared to these models, and can be observed from the following Figure 14, wherein these fMeasure values are plotted. Similar accuracy is observed for life expectancy evaluation, and can be observed from the following Table 5, wherein average accuracy (AAL) values for life expectancy are tabulated. Figure 12: Average precision for different algorithms from [7; 19; 28], and proposed work.