## 1 Introduction  
Chronic obstructive pulmonary disease (COPD) causes over 31.7 lakh deaths globally every year [1] and affects over 2.51 crore individuals. Over 90% of these deaths occur in developing countries such as India, Bangladesh, Sri Lanka, etc., wherein patients cannot go for high-cost repetitive tests. Due to conditions such as emphysema going undetected for a long duration, chronic lung conditions such as COPD are caused. Patients with COPD have an average life expectancy of 5 years, which is another major cause of concern, because these patients require continuous medical care, which causes burden on the entire healthcare staff. These conditions can be detected in their early stages from single X-ray scans via complex image processing models. These models perform the following steps in order to evaluate presence of COPD and other chest related diseases. High efficiency image acquisition & processing, wherein effective pre-processing models are deployed for noise reduction, filtering and image enhancement. These models aim at reducing effects of gaussian, power line and other noise types from the image. Moreover, any kind of enhancement which is needed is also done via these models. The enhancements assist in effective region of interest extraction from input images, which can be used for improving classification efficiency of the system. Pre-processed images are given to segmentation models, wherein all the regions of interest are extracted. Algorithms such as saliency maps, adaptive threshold-based models, clustering models, texture-based segmentation models, etc. are used. These algorithms are applied depending upon image types, and are selected to be application specific for improved segmentation performance. A sample image depicting difference between healthy and COPD affected tissues can be observed in Figure 1.  
Each of the segmented regions are given to feature extraction blocks. These blocks represent these regions into numerical vectors, showing that vectors of one type of lung disease are different from other types of lung disease; while vectors of similar lung disease types have minimum difference. In order to evaluate this difference, variance, standard deviation, and correlation values are evaluated using the following Equations,  
\[V=\sqrt{\frac{\sum\left(f_{i}-\frac{\sum f}{N}\right)}{N-1}} \tag{1}\]  
where \(V\) is the variance,\(f\) is the feature vector and \(N\) are the number of images taken for training the system. \[C=\frac{\sum\left(f_{1i}-\frac{\sum f_{1}}{N}\right)*\left(f_{2i}-\frac{\sum f _{2}}{N}\right)}{\sqrt{\sum\left(f_{1i}-\frac{\sum f_{1}}{N}\right)^{2}*\sum \left(f_{2i}-\frac{\sum f_{2}}{N}\right)^{2}}} \tag{2}\]  
where \(C\) is the correlation coefficient, while \(f_{1}\) and \(f_{2}\) are the feature vectors of same or different lung disease types. The contribution of the paper are as follows:  
* In order to maximize variance and minimize correlation between features of different lung disease types, a feature selection engine is deployed. This engine uses algorithms such as principal component analysis (PCA), independent component analysis (ICA), etc. Figure 1: Difference between healthy and damaged lung tissues. The selected features are given to classification models like convolutional neural networks (CNNs), random forests (RF), support vector machines (SVMs), etc., for identification of disease types. The results of these models are post-processed via complex models like long-short-term-memory (LSTM) and gated recurrent units (GRUs) for performing temporal analysis on the classification results. These models aim at finding out results such as life expectancy and progression of disease. Upon post-processing, results are verified from medical practitioners, and depending upon their feedback, the system is retuned to improve its internal performance. Retuning involves modification of system weights, changing the algorithms used for processing, etc., and is continuously done until a specified threshold performance is achieved.