AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Abstract

Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

“…Many relevant algorithms have been developed in recent years to get good results. For example in [11] , X-Ray and CT scan imaging modalities are described in terms of their advantages and problems for COVID-19 detection, and the application of AI technology to COVID-19 detection is fully recognized as a valuable research. Beyond that, [12] fully acknowledges the role of medical images in the field of image segmentation, while it considers the technique as the initial and most important component of the diagnostic and therapeutic pipeline.…”

“…Medical image segmentation has been studied in the field of COVID-19 so far, and although many effective methods have been proposed, some problems have been found to be in urgent need of solution during the research process, and solving such problems is an excellent contribution to enhance the research results in this field. Despite the fact that the global outbreak region is extensive, data collection and tagging are difficult to complete in a short period of time due to high labor expenses and time constraints [11] , [38] . Weakly supervised learning is of particular research interest that are less labor and time compared to traditional training labeling methods.…”

“…Despite the fact that the global outbreak region is extensive, data collection and tagging are difficult to complete in a short period of time due to high labor expenses and time constraints [11] , [38] . Weakly supervised learning is of particular research interest that are less labor and time compared to traditional training labeling methods.…”

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

“…Deep learning is seen as a major future trend in COVID-19 prognosis [24] . The models like CNN, fully connected neural networks, sequence networks develop a perception of the severity through a layered stack of learnable units.…”

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

“…DL approaches are at the forefront of accurate COVID-19 diagnosis and prediction from medical imaging. It can be applied for the purpose of classifying the infected patients from the normal individuals or to segment infectious regions in CXR and CT images [36]. In this review, the research focused on deep learning-based approaches that used CXR or CT scan or both as a dataset in diagnosing COVID-19.…”