## 2 Literature Review
This accuracy can be further improved by using a transfer learning model similar to [7] which is proposed in [9], that uses ResNet101, ResNet18, SqueezeNet, and ResNet50 models for transfer learning. Accuracy of 99.82% is observed via the use of ResNet18 model with wavelet feature decomposition. This high accuracy is due to the use of 90% data for training, and only 10% for testing, it is recommended that researchers and system engineers should balance this dataset to 70:30 from 90:10 in order to evaluate the true performance of this model. Similar architectures based on CNN for detection of COVID-19 disease are described in [10; 11; 12]. Each of these architectures use a different variant of CNN like generative adversarial network (GAN), GoogLeNet, Quantitative pleural line analysis, etc. in order to detect traces of COVID-19 disease in the patient chest X-ray imagery. Segmentation of pulmonary lobe is of high significance when it comes to evaluation of lung diseases. The work in [13] proposes use of dual-attention V-network (DAVNet) for performing this task. The DAVNet uses a combination of encoders to extract features and uses a combination of decoding blocks for generation of segmentation results. An accuracy of 93.9% is achieved via the use of this model, which can be further improved by incorporation of transfer learning in the system. Such transfer learning and deep learning models are suggested in [14; 15; 16], wherein disease severity, ventilation issues and other lung diseases are predicted with more than 91% accuracy using standard CNN architectures. Lung diseases can also be evaluated using markers in Point-of-Care ultra-sound imagery. The work in [17] proposes a dual CNN model with Spatial Transformers Network (STN) in order to identify COVID-19 markers in lung ultrasounds. An accuracy of 75% is  
Figure 2: Transfer learning from pneumonia classification models to detect COVID-19 disease [7]. achieved using this method, which can be improved via transfer learning mechanisms. It can also be observed that ultrasounds do not contain enough visual information in order to identify lung diseases with expected accuracy levels for real-time applications. X-ray scans are a must for such applications, and their performance with CNNs is good even for highly complex conditions such as interstitial diseases as observed in [18]. Here, CNNs are used for identification of micronodules, ground glass opacity (GGO), consolidation, honeycomb reticulation, and a combination of GGO/reticulation with over 85% accuracy. The architecture uses combinations of Leaky Rectilinear Units (ReLU) with average pooling and fully connected softmax layers in order to perform this task. The accuracy of this system can also be improved via deep transfer learning, data augmentation or via 3D CNN models. Such 3D CNN models are proposed in [19], and are able to identify lung cancer at early stages via Gradient weighted class activation. An accuracy of 97.17% is achieved using this model on the standard LUNA 16 dataset. Hyperspectral imagery is captured from this dataset, and given to a 3D CNN as observed from Figure 3, wherein a cascaded combination of convolution and pooling is connected with dense neural networks in order to classify between healthy and disease classes. Other medical applications can also take advantage of this 3D CNN model, in order to improve their classification performance. The work in [20] lists out some of these applications related to COVID-19, and suggests that CNNs and their variants are fully capable of identification of these diseases with utmost accuracy. A generalized architecture for medical image classification using CNNs is described in [21]. In this work, researchers have used Visual Attention Mechanism based Multiscale network which is effective in extracting visual features from images. These features are given to a Mahalanobis distance vector function for obtaining an accuracy of 99.89% for classification lung nodules and breast cancer diseases.