## 3 Methodology
### Motivations  
DL algorithms may be a beneficial technique for detecting COVID-19 disease. DL techniques have been used to solve a variety of similar challenges, such as skin cancer classification [48], Parkinson and brain illness categorization [49], and pneumonia diagnosis using chest radiograph images [50]. In [51], the authors created a 121-layer DL-based model for pneumonia detection that produces the confidence score based on an input chest radiograph image. The model has been trained on millions of images and consistently outperforms practicing radiologists. It can detect 14 pathological disorders in chest radiographs with slight modifications using the most widely available chest radiograph dataset [52]. As COVID-19 has symptoms similar to severe pneumonia, therefore, inspired by the success of DL-based architecture in pneumonia detection using chest radiograph  
Figure 1: Abstract view of proposed CovidDetNet framework. images, we proposed a CovDetNet model for COVID-19 detection and classification in this work. The goal of this research is to propose a DL model that can effectively detect COVID-19 using chest radiographs with higher accuracy. The proposed model's depth and input image resolution are based on the following facts: it is widely assumed that a deeper DL-based model captures more complicated and vital deep features and increases the network's classification performance. Furthermore, many DL-based models utilized depth scaling to improve their accuracy [53; 54]. However, as the depth of the network expands, so does the computational complexity, which does not ensure that accuracy will improve in all circumstances. Therefore, DL-based models collect more detailed features with high-resolution input images to attain improved performance. DL models accept images with various resolutions, such as 224 \(\times\) 224, 227 \(\times\) 227, and 299 \(\times\) 299, but models with higher resolution tend to perform better in recognition [55]. Similarly, the proposed model has ten layers and can process images with a resolution of 256 \(\times\) 256 pixels. The CovidDetNet architecture and the size of the input image are chosen based on the present computing machine requirements. Furthermore, we used both cross-channel [56] and batch [57] normalization (BN) layers in the proposed architecture. Cross-channel normalization layer because cross channel normalization improves generalization and reduces top-1 and top-5 error rates [56]. Batch normalization accelerates the model's training by reducing internal covariate shifting [57]. Batch normalization can stabilize DL models while maintaining data dispersion (data distribution). The input distribution varies as we train the CovidDetNet DL model, making the model train slower (internal covariate). We employ batch normalization to retain the same data distribution and deal with covariate shifting by normalizing the results (mean = 0, standard dev = 1). Furthermore, batch normalization normalizes each feature such that its significance is maintained, although some classification features have a greater numerical value than others. As a result, the proposed network will be unbiased (to higher-value classification features). Furthermore, in contrast to a network that does not utilize batch normalization, the model that uses this technique is trained faster and has higher accuracy. Additionally, we used both LeakyRelu [58] and ReLu [59] activation functions in the proposed CovidDetNet model. Because ReLu is more computationally efficient, it only needs to select max (0, x) and not execute expensive exponential calculations. It indicates that ReLu neurons have zero derivatives for all negative inputs (output zero for negative values). Furthermore, a value of 0 indicates that the network will run faster on the negative axis. Moreover, we used the LeakyRelu [58] activation function in the first five layers to confound the dying ReLu issue. The ReLu activation procedure is extensively employed between layers to enhance non-linearity and handle more complicated information.