CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Javaheri, Tahereh; Homayounfar, Morteza; Amoozgar, Zohreh; Reiazi, Reza; Homayounieh, Fatemeh; Abbas, Engy; Laali, Azadeh; Radmard, Amir Reza; Gharib, Mohammad Hadi; Mousavi, Seyed Ali; Ghaemi, Omid; Babaei, Rosa; Mobin, Hadi Karimi; Hosseinzadeh, Mehdi; Jahanban‐Esfahlan, Rana; Seidi, Khaled; Kalra, Mannudeep K.; Zhang, Guanglan; Chitkushev, Lou; Haibe‐Kains, Benjamin; Malekzadeh, Reza; Rawassizadeh, Reza

doi:10.1038/s41746-021-00399-3

Cited by 91 publications

(66 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the authors of [ 41 ] established a DL method named CovidCTNet to diagnose COVID-19 infection from CT images. This work applied U-Net model for developing BCDU-Net architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

2021

View full text Add to dashboard Cite

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

2021

View full text Add to dashboard Cite

show abstract

“…This study also implements Grad-CAM on ResNet layers for main lesion region visualization. Javaheri et al (2021) have introduced a multi-step pipeline of a deep learning algorithm, namely, CovidCTNet, to detect COVID-19 from CT images. Using controlled CT slides as a reference, the dual function of BCDU-Net ( Azad et al, 2019 ) in terms of anomaly detection and noise cancellation was exploited to differentiate COVID-19 and community-acquired pneumonia anomalies.…”

Section: Covid-19 Detection and Diagnosismentioning

confidence: 99%

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Deng

2021

Front. Artif. Intell.

View full text Add to dashboard Cite

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients’ check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

show abstract

“…In CT images, the lungs are segmented to focus on the infectious regions [50]. Segmentation tools include U-Net, VB-Net, BCDU-Net and V-Net which are used in [51], [52], [53] and [54], respectively. In some cases, image quality issues such as low contrast (Figure 3(a)) introduces challenges in the detection process.…”

Section: Artifacts/textual Data and Low Contrast Imagesmentioning

confidence: 99%

“…[51,80,81,95]. In [53], authors proposed COVIDCT-Net where they used BCDU-Net [102] to segment infectious areas before feeding it into a CNN for classification. DeCovNet was proposed in [51] where a pre-trained U-Net [103] is used to segment the 3D volume of the lung image before being fed into a deep CNN architecture.…”

Section: Multi-view Representation Learningmentioning

confidence: 99%

“…Probably, the most famous derivations of U-Net is VB-Net [97], which is used in [52], [96], [98], and [100]. There are other architectures available for seg-mentations such as V-Net [99] used in [54], BCDU-Net [102] used in [53] and other works like [56], [81] and [94] that have incorporated image segmentation into their deep models rather than going through segmentation as a preprocessing step.…”

Section: Focal Loss and Weighted Lossmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Rahman

Sarker

Miraj

et al. 2020

Preprint

View full text Add to dashboard Cite

In the updated version of the paper, more experiments and result are illustrated. We also introduce a new chapter (Image Preprocessing) in Section 7. We also added two new tables (Table 4 and Table 6), one figure (Figure 4) and renewed Table 1, 2, and 7 and Figure 5. We also edited many things throughout the paper: rewriting Abstract, Data Description, and Conclusion; Adding more datasets description in Section 5; updating method and analysis of result. The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment is the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

show abstract

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Cited by 91 publications

References 44 publications

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Contact Info

Product

Resources

About