## 1 Introduction
An important aspect of home tests for COVID-19, which are relatively simple to perform and interpret, is the immediacy of test results, between 10 and 30 min. Previously, patients had to wait several days or more for results from commercial reference laboratories. There are several important issues regarding using and interpreting home COVID-19 tests. Foremost among these are obtaining a quality specimen and the performance of the test. The process for the consumer has been simplified by the manufacturer-provided visual aids, videos, or online guidance to assist in specimen collection and understanding test performance. For home emergency use, authorization from the Food and Drug Administration (FDA) required feasibility data showing that people in the authorized age ranges can safely and accurately perform these tests. Individuals performing these tests must read and follow the manufacturer's instructions. Regardless of the test type, whether performed at home or in the laboratory, an inferior quality specimen often translates into inferior test results. Furthermore, positive results in asymptomatic individuals are less accurate and should be confirmed by more accurate tests. Additionally, home-based COVID-19 testing is not common globally, especially in underdeveloped countries. For example, currently, there is not a home-based COVID-19 testing facility in Pakistan. In contrast, the PCR-basedCOVID-19 facility is frequently available globally but is time-consuming compared to the proposed approach. Furthermore, COVID-19 has been reliably detected in its initial stages using a variety of medical imaging modalities, including chest radiography, electrocardiogram trace images, and computed tomography (CT) scan. Medical practitioners place a higher value on chest radiograph images since they are easily accessible through radiology departments. According to radiologists, chest radiograph images aid in the precise knowledge of chest pathology. As a result, the X-ray modality [21] is the first low-cost and low-risk approach for the COVID-19 analysis. The X-ray approach is an extensively utilized, most effective and accessible tool to identify and diagnose pneumonia [22]. Pneumonia is an infection that inflames the lungs' air sacs. X-ray serves an important role in clinical care and epidemiological studies. Detecting pneumonia in chest X-rays, on the other hand, is a difficult task that requires the presence of professional radiologists. We offer a model that can detect pneumonia from chest X-rays more accurately than experienced radiologists in this paper. X-rays are simpler, quicker, inexpensive, less harmful, and expose individuals to less radiation than CT and magnetic resonance imaging (MRI) [23]. Recent research has shown that Artificial Intelligence (AI) methods employing deep learning (DL) methodologies can identify numerous disorders on chest radiographs with accuracy comparable to expert radiologists [24; 25; 26; 27]. When experienced radiologists are unavailable, these computer-aided detection (CADs) systems can help with imaging-based patient classification in resource-constrained situations and increase practitioner's chest radiograph interpretation accuracy and inter-reader variability [28; 29]. Deep learning-based CAD has been shown in several recent studies to detect and identify COVID-19 on chest radiographs with high (radiologist-level) accuracy and to use it in medical practice [30; 31]. DL approaches employ unstructured data, extract more significant features automatically [32], and generate more accurate results than classic ML techniques. Several investigations were started at the beginning of 2020 to build automated DL models for reliable COVID-19 detection [33]. Convolutional neural networks (CNNs) were employed in most of this research to classify and assess COVID-19-infected or normal chest X-ray pictures. To detect and identify brain tumors from magnetic resonance images (MRI) data, specialists can use CAD based on classical DL [34].