## 2 Related Work
Their method of using a weakly supervised network was capable of developing self-attentive techniques on all chest X-rays which helps qualified technicians' predicting accuracy. A study carried out by [PERSON] [28] shows the major diagnostic techniques that are being clinically used to detect COVID-19 includes RT-PCR, which is highly efficient but requires more time and trained labor. The CRISPR method is a cheap alternative which requires less time to produce results, although this technique has a high risk of sample contamination. Molecular POC is a faster and cheaper technique which may not require RNA extraction. However, it is prone to low accuracy outcomes in the first five days after symptoms are identified. NGS is a popular technique used to detect viral variant and other infections, though it is an expensive method and requires more time compared to other diagnostic methods. A binary-based CNN model was developed by [PERSON] [29] to classify COVID-19 and normal chest X-ray images. The proposed model resulted in 97.56% of accuracy. It was claimed that despite the model having better accuracy, it can be further improved by increasing the dataset for better training of the model. The study compared the performance of its model to two other models and resulted in better accuracy than the compared models. However, the model used in the study was trained on a small dataset containing 165 COVID-19 and 165 normal X-rays, which can be further improved to work on larger datasets for better accuracy. A study performed by [PERSON] [30] shows a robust method for automated detection of COVID-19 using X-ray images. The proposed method uses binary and multiclass prediction models with image augmentation to provide comparison with models without image augmentation. The study shows how a CNN model with image augmentation can improve their reliability and accuracy. The proposed method uses pretrained models. Adding image augmentation can greatly improve speed and accuracy, which are helpful in COVID-19 diagnosis, and resulted in an accuracy of 99.7%. However, the model is a 121-layer variant which is complex and hard to train as it requires capable resources to train and implement the model on a large-scale. Many attempts have been made to diagnose COVID-19 using chest X-ray images, as seen in the preceding discussion, but the developed models are based on a complex architecture, which means the models demand a large number of datasets to be trained in order to achieve more accuracy. The models also require time for training and predicting the outcomes as the models are based on high numbers of parameters and layers. Furthermore, the proposed models are trained on datasets which are not equally distributed. Unequal distribution of data can give rise to the class imbalance problem which may lead to wrong predictions. The novelty of the research is that we proposed a customized CNN model with comparatively less layers so that the model is trained with less datasets to provide efficient predictions and accuracy. The model contains fewer parameters so it could predict accurate results in a timely fashion. Cross-dataset experiments were carried out in order to test the efficiency of the model. The customized CNN model was trained and validated on equally distributed data of COVID-19 and normal X-ray images to fix the class imbalance issue. The proposed method of using CNN with chest X-rays requires no trained staff, is highly economical as X-ray machines are readily available in every hospital, and can deliver output instantly. This method can be deployed in most of the remote locationswhere RT-PCR and other diagnostic tools are unavailable. Using the CNN-based model with its high output capacity, it is possible to detect hundreds of results in a day, which might not have been possible with other techniques for early diagnosis.