## 2 Literature Review
Another application for detection of COVID-19 and other lung diseases using CNNs is described in [22; 23], the proposed architecture uses VGGNet model in order to obtain an accuracy between 85 to 93% across different datasets. Lung diseases can also be detected using effective content-based image retrieval (CBIR) models. The work in [24] proposes use of s vector machine (SVM) with adaptive thresholding-based segmentation in order to retrieve lung disease images similar to query image from a database of over 20,000 images. The system uses nodule filtering with linear feature extraction engine to perform image ranking for effective retrieval. An accuracy of 90% is achieved with the help of this classification-based retrieval engine. This accuracy can be improved via the use of modular CNN architectures, wherein each architecture is responsible for identification of a different lung disease. The work in [25; 26; 27], propose the use of such architectures in a wide variety of lung diseases, which include but are not limited to interstitial lung disease, pulmonary diseases, and COVID-19 disease and its variants with more than 91% accuracy. Apart from using these generalized architectures  
Figure 3: Model of 3D CNN for effective classification [19]. for detection of lung diseases, the work in [28] proposes designs of an infection-based convolutional neural network or Inf-Net. This network is specifically designed to detect and localize lung diseases, and uses combination convolutional layers with reverse attention (RA) blocks and upsampling in order to perform the final prediction. Architecture for this system can be observed from Figure 4, wherein a cascaded and fully connected version of this network is shown. The architecture is able to detect lung diseases with an accuracy of 89%, which is low considering generalized CNN architectures have higher accuracy than this specialized architecture. The accuracy can be improved by integration of transfer learning and multi dimension attention mechanism, in order to reduce the probability of classification error in this network. Such a network can be observed in [29], wherein fine-grained lung cancer detection is done using Multidimensional Attention Mechanism that uses an extensive feedback mechanism via dense networks in order to obtain a classification accuracy of 92% across one million images. These images are handled using a big-data image management system as described in [30], wherein Hadoop architecture is used in order to improve the efficiency of handling a large number of images. Chronic obstructive pulmonary disease or COPD is another deadly lung disease which can be detected using X-ray scans. The work in [31] proposes a Deep CNN architecture that uses Multi-View imagery from lung airway tree in order to identify presence of COPD. A total of nine different CNN architectures are deployed, each of which uses either a different view or a different pixel level for classification. The architecture decomposes captured images into colored, gray, and binary levels. These images are captured via different views, namely ventral, dorsal, and isometric. A singular VGGNet based CNN architecture is reused by all these different image variants, and classification is done into COPD and healthy chest X-ray classes. An accuracy of 91% is achieved using this model, which is moderate considering the fact that the architecture passes input imagery through 9 different CNN processing units for final classification. It is recommended that this architecture be replaced by a less complex and high accuracy Generative Adversarial Network (GAN) as suggested in [32]. This architecture is also based on CNNs, and is able to identify lung diseases via segmentation with an accuracy of over 95%. Other architectures as suggested in [33; 34; 35] use similar variants of CNNs in order to detect pulmonary lung diseases with more than 90% accuracy. This accuracy performance can be improved by adding multimodality in CNN models for lung image classification.