COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers

Abstract

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

“…Over the past few years, there has been a surge in studies exploring deep learning techniques to diagnose COVID-19 and pneumonia. Systematic reviews of AI-enabled COVID-19 detection can be found in [1][2][3] in 2021, [4][5][6][7][8][9][10] in 2022, and [11][12][13] in 2023. While all reviews delved into deep learning neural networks and medical image databases, there were apparent shifts in focus, from the feasibility of deep learning to this aim and limited databases in 2021, to model performance comparison and database survey in 2022, and to model enhancement/development and real-world applications recently.…”

“…These studies either used chest X-ray images or CT scans [17][18][19][20][21][22] and were designed as either binary or multi-class classifications [23,24]. In general, the model classification accuracy was higher using chest X-ray images (90%+) than using CT (~85%) and was higher (1-5%) in binary than multi-class classifications [10,25,26]. To remedy data shortages, various techniques of data augmentation were used, including scaling, rotation, shifting, and image filtering such as grayscale and Gaussian blur [27][28][29].…”