[論文レビュー] Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Afshin Shoeibi, Marjane Khodatars+13 more|arXiv (Cornell University)|Jul 16, 2020

COVID-19 diagnosis using AI参考文献 240被引用数 148

ひとこと要約

COVID-19 の検出を X 線/CT からの深層学習モデルの検出、セグメンテーション、予測まで、データソースと課題を含む包括的な調査。

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, artificial intelligence (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 machine learning (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.

研究の動機と目的

• X線およびCT画像を用いたCOVID-19検出とセグメンテーションに対するDLネットワークの適用を要約する。
• 地域ごとにCOVID-19拡散を予測するためのDLベース予測手法をレビューする。
• DL COVID-19研究で用いられる公的データセットと前処理の実践を特定する。
• DLベースのCOVID-19診断と予測における課題と今後の研究方向性を議論する。

提案手法

• COVID-19研究で分類、セグメンテーション、予測に用いられるDLアーキテクチャの調査。
• 限られたデータを緩和するためのデータ拡張と事前学習モデルの議論。
• U-Net、SegNet、FCN などのセグメンテーションモデルの概要と肺の境界描写における役割。
• RNN、LSTM/GRU などの予測モデルの説明。
• 先進的なAIトレンド（アテンション、トランスフォーマー、データフュージョン、グラフDL）の探索。

実験結果

リサーチクエスチョン

• RQ1 X線およびCT画像からのCOVID-19検出に最も効果的だったDLアーキテクチャとトレーニング戦略は何か？
• RQ2 セグメンテーションと予測のDLアプローチはCOVID-19診断と拡散予測にどのように貢献してきたか？
• RQ3 DL COVID-19研究で一般的に使用される公的データセットと前処理の手順は何か？
• RQ4 COVID-19検出と予測へのDL適用における主要な課題と今後の方向性は何か？

主な発見

• DL-based CADS using X-ray/CT achieve high accuracies across numerous studies (various reported metrics).
• Segmentation models like U-Net and SegNet are commonly used for lung delineation in COVID-19 imaging.
• Forecasting DL approaches mainly use RNN/LSTM/GRU to model time-series data for spread prediction.
• Attention, transformer, data fusion, and graph DL methods are emerging trends for COVID-19 diagnosis and prognosis.
• There is a rich set of public datasets and data augmentation strategies to address limited COVID-19 data, though forecasting data remain relatively scarce.