# CT-Scan for Covid-19 Classification using Machine Learning ## Dataset * [UCSD-AI4H/COVID-CT](https://github.com/UCSD-AI4H/COVID-CT) * [Paper](https://www.medrxiv.org/node/76881.external-links.html) * [ieee8023/covid-chestxray-dataset](https://github.com/ieee8023/covid-chestxray-dataset) * [Kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) * [Dataset: CT Scan for Covid Classification](https://www.graviti.com/open-datasets/dataset-detail/CT_Scans_for_COVID-19_Classification) * [NCOV China](http://ncov-ai.big.ac.cn/download) * [haydengunraj/COVIDNet-CT: COVID-Net Open Source Initiative - Models and Data for COVID-19 Detection in Chest CT](https://github.com/haydengunraj/COVIDNet-CT) * [DAtaset](https://www.kaggle.com/hgunraj/covidxct) : three categories (Covid, Control, Pneunomia) * [COVID-19 CT Lung and Infection Segmentation Dataset - Zenodo](https://zenodo.org/record/3757476#.X62Iw2hKiUk) * [Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets - Nature Communications](https://www.nature.com/articles/s41467-020-17971-2) * [Clara Deploy AI COVID-19 Classification - NVIDIA NGC](https://ngc.nvidia.com/catalog/containers/nvidia:clara:ai-covid-19): two categories (Covid, Normal) * [LIDC-IDRI - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) ## Notes * [Kaggle](https://www.kaggle.com/azaemon/preprocessed-ct-scans-for-covid19) * [Adrian Rosenbrerck](https://www.pyimagesearch.com/2020/03/16/detecting-covid-19-in-x-ray-images-with-keras-tensorflow-and-deep-learning/) [GitHub Repo, work in progress](https://github.com/AleGiovanardi/covidhelper) * [PaperswithCode](https://paperswithcode.com/paper/covid-ct-dataset-a-ct-scan-dataset-about) ## Github * [AlexTS1980/COVID-CT-Mask-Net](https://github.com/AlexTS1980/COVID-CT-Mask-Net) : Segmentation and Classification, category (COVID, pneumonia, normal), Mask R-CNN. * [Presentation](https://github.com/AlexTS1980/COVID-CT-Mask-Net/blob/master/presentations/COVID_19_Presentation_Kent.pdf) * [Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans](https://www.medrxiv.org/content/10.1101/2020.10.30.20223586v2.full.pdf) * [Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19](https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v2.full.pdf) * [COVID-CT-Mask-Net: Prediction of COVID-19 From CT Scans Using Regional Features](https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v2.full.pdf) * [bkong999/COVNet](https://github.com/bkong999/COVNet) * [Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/#SD1) * [Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy](https://pubs.rsna.org/doi/10.1148/radiol.2020200905) * [JordanMicahBennett/SMART-CT-SCAN\_BASED-COVID19\_VIRUS\_DETECTOR](https://github.com/JordanMicahBennett/SMART-CT-SCAN_BASED-COVID19_VIRUS_DETECTOR), historical project related CT scan usage for Covid Classification * [Covid-Net](https://github.com/lindawangg/COVID-Net), complete covid-net project (Covid-Net, CovidNet-S, CovidNet-CT, COVIDNet-CXR) * [COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images](https://www.nature.com/articles/s41598-020-76550-z) * [COVIDNet-S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity](https://arxiv.org/abs/2005.12855) * [COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images](https://arxiv.org/abs/2009.05383), GitHub Repo [haydengunraj/COVIDNet-CT](https://github.com/haydengunraj/COVIDNet-CT) : COVIDNet-CT * [zeeshannisar/COVID-19](https://github.com/zeeshannisar/COVID-19) : CovidNet with different architecture pretrained * [iliasprc/COVIDNet](https://github.com/iliasprc/COVIDNet) : Pytorch Implementation of CovidNET * [Jeremykhon](https://github.com/jeremykohn/rid-covid), complete list of project * [kaushikjadhav01/COVID-19-Detection-Flask-App-based-on-Chest-X-rays-and-CT-Scans](https://github.com/kaushikjadhav01/COVID-19-Detection-Flask-App-based-on-Chest-X-rays-and-CT-Scans) * [JunMa11/COVID-19-CT-Seg-Benchmark](https://github.com/JunMa11/COVID-19-CT-Seg-Benchmark) CT Scan Segmentation * [rekalantar/covid19\_detector](https://github.com/rekalantar/covid19_detector) * [aniruddh-1/COVID19\_Pneumonia\_detection](https://github.com/aniruddh-1/COVID19_Pneumonia_detection) * [rohilrg/COVID19-xray-classifier](https://github.com/rohilrg/COVID19-xray-classifier) * [KiLJ4EdeN/DeepCOVID](https://github.com/KiLJ4EdeN/DeepCOVID) * [sydney0zq/covid-19-detection](https://github.com/sydney0zq/covid-19-detection) * [Paperswithcode](https://paperswithcode.com/paper/automatic-detection-of-coronavirus-disease) * [paper/harmony-search-and-otsu-based-system-for](https://paperswithcode.com/paper/harmony-search-and-otsu-based-system-for) ## Paper * [A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia](https://www.sciencedirect.com/science/article/pii/S2095809920301636) * [Deep learning system to screen coronavirus disease 2019 pneumonia](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175452/) - similar with above paper * Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2, Xi Xu et al. * Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review, Jinkui Li et al. * CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients,\ Vineeta Ojha et al. * iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia, Wanshan Ning et al. [Project Site](http://ictcf.biocuckoo.cn/), [Europe PMC Open Access](https://europepmc.org/article/ppr/ppr141530) [Research Square](https://www.researchsquare.com/article/rs-21834/v1) * Computed Tomography (CT) Imaging Features of Patients with COVID-19: Systematic Review and Meta-Analysis, Ephrem Awulachew et al. [NCBI Open Access](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378588/) * Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning, Wanshan Ning et al., [Nature Open Access](https://www.nature.com/articles/s41551-020-00633-5) * Fast automated detection of COVID-19 from medical images using convolutional neural networks, Shuang Liang et al. [Research Square](https://www.researchsquare.com/article/rs-32957/v1) * A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset, Mohammad Rahimzadeh et al. [Medrxiv](https://www.medrxiv.org/content/10.1101/2020.06.08.20121541v3) [Dataset](https://github.com/mr7495/COVID-CTset) [Code](https://github.com/mr7495/COVID-CT-Code) * [Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans](https://www.medrxiv.org/content/10.1101/2020.10.30.20223586v2.full.pdf) * [Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19](https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v2.full.pdf) * [COVID-CT-Mask-Net: Prediction of COVID-19 From CT Scans Using Regional Features](https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v2.full.pdf) * [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://arxiv.org/pdf/2003.13865.pdf) [GitHub Repo](https://github.com/UCSD-AI4H/COVID-CT) * [Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans](https://www.medrxiv.org/content/10.1101/2020.04.13.20063941v1) * [Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans](https://www.medrxiv.org/content/10.1101/2020.06.08.20125963v1) [GitHub](https://github.com/arthursdays/HKBU_HPML_COVID-19) * A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan, [Arxiv](https://arxiv.org/abs/2006.14419) [GitHub](https://github.com/KiLJ4EdeN/COVID_WEB) * [Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks](https://arxiv.org/pdf/2004.07407.pdf) [GitHub](https://github.com/amobiny/DECAPS_for_COVID19) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://irosyadi.gitbook.io/irosyadi/machine-learning/covid-ct-scan-classification.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.