## 2 Literature Review  
Extracting and identifying malignant tissues from chest X-ray images requires a series of highly efficient image processing modules to work in tandem. These modules use complex computational models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Gated Recurrent Units (GRUs), etc., to perform this identification with high accuracy. For example, the work in [2] uses a VGGNet based CNN model along with Deep Convolutional Generative Adversarial Network for creation of augmented images. Using this model, an average accuracy of 99.34% is achieved for pneumonia detection across 12k images. This high accuracy is possible because of augmentation, due to which a larger image dataset is available for training. This concept can be extended to other lung diseases in order to improve their internal accuracy performance. For instance, the work in [3] again proposes use of VGGNet based CNN for detection of pneumonia in children with an accuracy of 96.47%, which is very high and can be used for real-time purposes. This work can also use augmentation for improving its internal performance. Apart from VGGNet architecture, other CNN models such as InceptionResNet, MobileNet_V2, and ResNet50 can also be used for pneumonia detection. The work in [4] proposes combination of all these architectures in order to produce an accuracy of 94.84% for classification into lung disease classes such as viral/normal, bacteria, and COVID-19. A total of 6000 images were used for this evaluation, which can be further extended in order to evaluate real-time performance of the system. This is needed because the study in [5] suggests that recent work in COVID-19 detection using X-ray images has limited accuracy due to unavailability of image datasets. Thus, in order to evaluate any classification model, it is necessary to identify prevailing datasets for the given application. For instance, the work in [6] does a comparative analysis of different COVID-19 based lung disease detection models, and suggest that using a large COVID-19 lung image dataset (such the one available at [[https://github.com/rgbnihal2/COVID-19-X-ray-Dataset](https://github.com/rgbnihal2/COVID-19-X-ray-Dataset)]([https://github.com/rgbnihal2/COVID-19-X-ray-Dataset](https://github.com/rgbnihal2/COVID-19-X-ray-Dataset)), accessed on 10 May 2022), CNN models like DenseNet201 combined with Quadratic SVM classifier as the fully connected layer, can achieve accuracy levels of 98.16% for a wide variety of images. The accuracy of CNN models can be improved via the use of transfer learning, wherein training information obtained from one dataset can be used to accurately classify images from other datasets. Work in [7] proposes use of Haralick features with transfer learning in order to learn from pneumonia classification models, and train them to perform COVID-19 detection. Architecture of this transfer learning model can be observed from Figure 2, wherein chest X-ray images are fed to a data augmentation layer. Operations like histogram equalization and wiener filtering are used for extraction region of interest. These regions are given to Haralick texture feature extraction model, which extracts highly efficient Haralick coefficients from the input image. Using values of these coefficients, and via VGGNet, ResNet, and Inception models, training models that match the given coefficients are evaluated. Classification is done by the evaluated models, and detection of pneumonia and other diseases is done. Learnings about these diseases are transferred for COVID-19 prediction, and the underlying infected regions are detected with 93% accuracy. A multi-feature CNN model for COVID-19 detection is proposed in [8], which can evaluate presence of the disease with 99% accuracy. The model uses a combination of original chest X-ray and enhanced chest X-ray images and trains a multi-feature CNN network based on AlexNet, ResNet50, SonoNet64, XNet, InceptionV4, and EfficientNetB4 models for improved classification performance.