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  "fetched_at": "2026-10-19T09:04:30Z",
  "source_url": "https://www.kaggle.com/competitions/covid-19-ai-detection-challenge/data",
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      "name": "data-description",
      "content": "# **Data**\nThe competition data include Computed Tomography (CT) scan images. Each image has one label that describes a clinical state of an individual.\nThe competition labels consist of three types:\n-**COVID-19** label indicates whether experts detect the presence of COVID-19 lesion.\n-**NON-COVID-Normal** label indicates the normal clinical outcome diagnosed by the medical experts.\n-**Pathological-NON-COVID** label indicates the presence of a disease other than COVID-19 detected by experts.\n\nThe competition data were collected from **Habib Bourguiba Hospital of Sfax** in 2022, thereby approved by medical experts.\n\nThe competition data are organized into three distinct sets: training, validation, and test sets. All labels are included in the different sets. We have publicly released 80% of the dataset as the training set for the competition, and we have retained the remaining 20% as test set.\n\n**# Files**\ntrain - Training images\ntrain_label.csv - Tagged class for an image in train folder\ntest - Test Images\nsample_submission.csv - a sample submission file in the correct format",
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      "name": "Description",
      "content": "\n**# Motivation**\nCOVID-19 has profoundly impacted the world we live in. We must adapt to this news and we need an exceptional initiative in innovation across the globe. Around the world, industry, academia, governments, and non-profit organizations are joining forces to fight the pandemic. These partnerships are paramount in struggling with the disease and investigating options that meet the varying needs of patients and healthcare professionals.\n\n**# Task Description**\nThe 7th IDSS (Intelligent Decision Support Systems) 2022 [**COVID-19 AI Detection Challenge**](https://itsis.org/?page_id=34) in conjunction with the IEEE International Conference on Information Technologies and Smart Industrial Systems [**ITSIS**](https://itsis.org/) 2022 invites participants to help medical professionals around the world quickly diagnose COVID-19. Participants\nare asked for building models to detect COVID-19 using Computed Tomography (CT) scan images and classify images into COVID-19, NON-COVID-Normal, and Pathological-NON-COVID.\n\n\n\n## Acknowledgements\nWe thank all team members for providing this dataset, namely Zeineb Fki, Boudour Ammar, Rahma Fourati, Hela Fendri, Amir Hussein, and Mounir Ben Ayed.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Accuracy]\n\n## Submission Format\n\nSubmission Format\nFor every unique combination in the dataset, submission files should contain two columns: Id and Class.\n\nThe file should have the following format:\nId (the same column index in the test set file) column and Class column.\n\nThe Class column contains values that can be 0, 1, or 2 which correspond to COVID-19, NON-COVID-Normal, and Pathological-NON-COVID classes, respectively.",
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    {
      "name": "rules",
      "content": "You have to respect these rules:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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