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Artificial Intelligence (AI) has promoted countless contributions in the field of healthcare and medical imaging. In this paper, we thoroughly analyze peer-reviewed research findings/articles on AI-guided tools for Covid-19 analysis/screening using chest X-ray images in the year 2020. We discuss on how far deep learning algorithms help in decision-making. We identify/address data collections, methodical contributions, promising methods, and challenges. However, a fair comparison is not trivial as dataset sizes vary over time, throughout the year 2020. Even though their unprecedented efforts in building AI-guided tools to detect, localize, and segment Covid-19 cases are limited to education and training, we elaborate on their strengths and possible weaknesses when we consider the need of cross-population train/test models. In total, with search keywords: (Covid-19 OR Coronavirus) AND chest x-ray AND deep learning AND artificial intelligence AND medical imaging in both PubMed Central Repository and Web of Science, we systematically reviewed 58 research articles and performed meta-analysis.

Medical X-rays are one of the primary choices for diagnosis because of their potential to disclose previously undetected pathologic changes, non-invasive qualities, radiation dosage, and cost concerns. There are several advantages to creating computer-aided detection (CAD) technologies for X-Ray analysis. With the advancement of technology, researchers have lately used the deep learning approach to obtain high accuracy outcomes in the CAD system. With CAD, computer output may be utilized as a backup option for radiologists, assisting doctors in making the best selections. Chest X-Rays (CXRs) are commonly used to diagnose heart and lung problems. Automatically recognizing these problems with high accuracy might considerably improve real-world diagnosis processes. However, the lack of standard publicly available datasets and benchmark research makes comparing and establishing the best detection algorithms challenging. In order to overcome these difficulties, we have used the VinDr-CXR dataset, which is one of the latest public datasets including 18,000 expert-annotated images labeled into 22 local position-specific abnormalities and 6 globally suspected diseases. To improve the identification of chest abnormalities, we proposed a data preparation procedure and a novel model based on YOLOv5 and ResNet50. YOLOv5 is the most recent YOLO series, and it is more adaptable than previous one-stage detection algorithms. In our paper, the role of YOLOv5 is to locate the abnormality location. On the other side, we employ ResNet for classification, avoiding gradient explosion concerns in deep learning. Then we filter the YOLOv5 and ResNet results. The YOLOv5 detection result is updated if ResNet determines that the image is not anomalous.