Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review (2007.10785v7)

Abstract: Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, AI methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional ML. Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed.

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Comprehensive Review

The reviewed paper provides an extensive examination of the application of deep learning (DL) techniques in the automated detection and forecasting of COVID-19 using medical imaging. The paper focuses on the use of X-ray and computed tomography (CT) images for diagnostic purposes and explores methodologies to predict the prevalence of COVID-19 through deep learning models. This review encapsulates various contributions in the field, highlighting the intersection of AI and medical imaging to address the challenges posed by COVID-19.

Overview of Key Contributions

1. Deep Learning Models for COVID-19 Detection: The paper covers several DL architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models including combinations like CNN-RNN. Specific models such as COVID-ResNet and COVIDiagnosis-Net are discussed with performance metrics that demonstrate high diagnostic accuracy and recall rates.
2. Segmentation Techniques: The challenges of segmenting lung regions in CT and X-ray images are tackled using models like U-Net and Res2Net, which have shown efficacy in delineating areas of interest with improved Dice coefficients and sensitivity metrics.
3. Forecasting COVID-19 Spread: For forecasting, the paper reviews various temporal models, including LSTM-based approaches, to predict pandemic spread. These methods leverage time-series data to produce accurate predictions with metrics such as RMSE and map accuracy.
4. Public Datasets and Tools: A multitude of public datasets are employed to train and validate these models, such as the COVIDx dataset for X-ray images and the UCSD-AI4H dataset for CT scans. Popular deep learning frameworks like TensorFlow and PyTorch are utilized across models for implementation.

Implications and Challenges

The implications of this research are significant for the future of AI in healthcare. The use of DL for the detection of COVID-19 promises quicker, more reliable diagnostic processes crucial in pandemic management. However, the paper highlights several challenges. Notably, the limited number of public datasets with comprehensive and labeled data hampers the training of more generalized deep learning models. Moreover, the variability in the quality of publicly available datasets introduces a challenge in building models that are robust across diverse populations and imaging conditions.

Future Directions

Moving forward, advancement in this domain could focus on the fusion of multimodal data to enhance model robustness. Additionally, developing models that incorporate phenotypic information might enhance predictive accuracy. Attention mechanisms and transformer models could further improve the interpretability and accuracy of these models, providing clinicians with tools that not only aid diagnosis but also provide insights into the decision-making process.

In conclusion, the paper provides a significant overview of DL methodologies applied to COVID-19 diagnosis and forecasting, illustrating the potential and current limitations in deploying these technologies in clinical settings. Encouragingly, it sets the stage for continued research in optimizing and standardizing AI-driven diagnostic tools during and beyond the COVID-19 era.