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.

Datasets

• 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

Working

Preprocessing

Preprocessing includes resizing, converting to grayscale, normalization of xray image etc.

Validation

An xray validator (classifier) checks whether the image is an xray image or not.

Lungs Segmentation

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.

Covid/Pneumonia/Normal Classification

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.

How to use

• Install Python

  • Download and install python 3.9 from python.org

• Get the code

  git clone https://github.com/ayush9304/Covid19-Detector
  

• Install Python dependencies

  pip3 install -r requirements.txt
  

• Run

  py manage.py runserver
  

• Explore

  • Goto http://127.0.0.1:8000/ url on any web browser

License

Licensed under the MIT License.