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Unlock the Power of AI with Our Chest X-Ray COVID-19 Detection Challenge!

Join the forefront of biomedical innovation and build a GPU-based deep learning model for multi-class classification of COVID-19, normal, and pneumonia cases. Outperform recent studies and make a valuable contribution to the research community by helping to accurately detect COVID-19 infections in their early stages, at an affordable cost.


Sign-up today at https://app.trustii.io



Contexte :

The COVID-19, also known as coronavirus disease 2019, is a viral illness caused by the SARS-CoV-2 strain of coronavirus. It originated in Wuhan, China in late 2019 and has since become a global pandemic, as declared by the WHO on March 11, 2020. Diagnosis of COVID-19 is typically done through RT-PCR testing, and chest X-rays can also be used to help with early detection of the disease.


Dataset :

It is a medical images directory structure containing Chest X-ray (CXR) Images of COVID19, NORMAL, PNEUMONIA. All images are preprocessed and resized to 256x256 in PNG format.

The dataset is a multi classification, it has three labels COVID, NORMAL and PNEUMONIA.


Competition :

Detect and classify COVID19 and Pneumonia from Chest X-Ray Images using Deep Learning methods.

If you are not familiar with images processing and deep learning you can take a look at few notebooks at Kaggle that handle chest x-ray images : https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia/code


Timeline :

The competition will open Thursday February 8th, at 8AM CET time. And it will be open until March 12nd.


Prize :

1st prize : 300 euros

2nd prize : 200 euros


Notebook GPU :

Trustii.io offers for free GPU based jupyter notebook, that you MUST use to build your model.

The notebook offers per user :

  • 16 Gb GPU (Tesla T4)

  • 4 vCPU

  • 28 Gb RAM

  • 100 Gb persistant storage

Trustii.io notebook comes with pre support of PyTorch, Tensorflow and Keras.


References :

  1. Shastri, S., Kansal, I., Kumar, S. et al. CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks. Health Technol. 12, 193204 (2022). https://doi.org/10.1007/s12553-021-00630-x

  2. Kumar S, Shastri S, Mahajan S, et al. LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images. Int J Imaging Syst Technol. 2022;117. DOI: https://doi.org/10.1002/ima.22770

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