## 1 Introduction
CNNs are commonly used in image classification and identification applications such as MRI brain cancer image classification [35] and others. According to our knowledge, when it comes to AI-driven tools that use imaging techniques, COVID-19 does not have a lot of state-of-the-art literature. Existing COVID-19 detection research has certain limitations, i.e., low accuracy of COVID-19 detection. Most studies relied on datasets with fewer images (small datasets). There are fewer training data, the model is not perfectly generalized, and the training samples may have been overfitted. Most studies use classic ML and transfer learning algorithms to detect and assess COVID-19. Still, the most concerning limitation of traditional ML (such as support vector machine (SVM)) is the long training time for large datasets. In contrast, the most concerning limitations in transfer learning systems are negative transfer and overfitting. One of the drawbacks of using pre-trained classification approaches is that they are frequently trained on the ImageNet database, which contains images that are unrelated to medical images. As a result, putting in place effective CADs to reliably and quickly identify COVID-19 from chest radiographs remains a difficult task. To address these limitations, the CovidDetNet DL model is proposed. It utilizes filter-based feature extraction, which can help achieve high classification performance. The proposed model has been created with a convolutional layer and both ReLu and Leaky ReLu activation functions, which extract the most detailed and important features from the chest radiograph images. The architecture can minimize many weight parameters by using a max-pooling operation. We implemented both Relu and leaky ReLu activation functions and batch and cross channel normalization operations in the proposed model, making it a novel COVID-19 detection and classification method. The proposed approach also tackles the issue of PCR kit scarcity by requiring only an X-ray machine, which is currently found in most hospitals across the world. As a result, people will not have to wait longer for colossal PCR kit shipments. The proposed approach would efficiently enable the contact and isolation of COVID-19 individuals and limit community transmission with the rapid detection of COVID-19. Therefore, as chest radiographs are low-cost and time-efficient, and available in practically every clinic, chest radiograph images were utilized as a sample dataset in this study. The suggested architecture was validated using a standard Kaggle (publicly available) dataset. According to the results, the proposed structure performs satisfactorily in COVID-19 detection in test accuracy. The main contributions of this research are:  
* A CovidDetNet DL model is proposed to detect COVID-19 positive cases using chest radiograph images. * Chest radiographs are favored over CT scans since X-ray machines are readily available in most hospitals and CT scans emit less ionizing radiation. * To evaluate the efficacy of the proposed model, we compared the proposed CovidDetNet model performance with hybrid approaches on the same dataset and experimental configuration. For this purpose, we utilized various classification metrics, i.e., accuracy, precision, recall, and f1-score. * We evaluated the performance of the proposed novel CovidDetNet DL model on a standard COVID-19 radiography database. The remainder of this paper is arranged as follows. Section 2 presents the related work about COVID-19 detection methods. The motivation and description of the proposed work are discussed in Section 3. Section 4 explains the datasets, evaluation measures, and experimental results. Section 5 discusses the proposed approach and explains the future directions and Section 6 concludes the paper.