Coviscan is an automated imaging tool which process chest x-ray images and predicts whether the person has Covid-19 disease or Pneumonia or Normal.
It uses deep learning algorithm Convolution Neural Networks (CNNs) also known as ConvNets to process and extract features from X-Ray images. The whole pipeline involves several steps like preprocessing, semantic segmentation, classification etc.
- https://www.kaggle.com/nikhilpandey360/chest-xray-masks-and-labels
- https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
- https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
- https://github.com/agchung/Figure1-COVID-chestxray-dataset
- https://github.com/ieee8023/covid-chestxray-dataset
Preprocessing includes resizing, converting to grayscale, normalization of xray image etc.
An xray validator (classifier) checks whether the image is an xray image or not.
Using U-Net architecture model, we segments out the lungs from the xray image. We used dice coefficient as loss function.
This model was able to achieve a dice score of 0.9621
in training data and 0.9611
on test data. The model was trained for 46 epochs.
A CNN model predicts whether the patients have Covid or Pneumonia or Normal. We tested on three different CNN architectures (i.e., MobileNetV2, EfficientNetB2, DenseNet121) for this task. The DenseNet121 gave the best result with the accuracy of 95.67%
. The accuracy of MobileNetV2 & EfficientNetB2 models was 92.64%
and 86.37%
respectively.
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Install Python
- Download and install python 3.9 from python.org
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Get the code
git clone https://github.com/ayush9304/Covid19-Detector
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Install Python dependencies
pip3 install -r requirements.txt
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Run
py manage.py runserver
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Explore
- Goto
http://127.0.0.1:8000/
url on any web browser
- Goto
Licensed under the MIT License.