tejas1304 / COVID-19-Detection-Flask-App-based-on-Chest-X-rays-and-CT-Scans

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

COVID-19-Detection-Flask-App-based-on-Chest-X-rays-and-CT-Scans

COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. After training, the accuracies acheived for the model are as follows:

                InceptionV3  VGG16   ResNet50   Xception
Chest X-rays    96%          94%      83%       92%

CT Scans        93%          93%      80%       95%

A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

The article for the project was selected and published in Towards Data Science:
https://towardsdatascience.com/covid-19-detector-flask-app-based-on-chest-x-rays-and-ct-scans-using-deep-learning-a0db89e1ed2a

Dataset

The dataset for the project was gathered from two sources:

1. Chest X-ray images (1000 images) were obtained from: https://github.com/ieee8023/covid-chestxray-dataset
2. CT Scan images (750 images) were obtained from: https://github.com/UCSD-AI4H/COVID-CT/tree/master/Data-split 80% of the images were used for training the models and the remaining 20% for testing

Evaluation and Results

Sample output of test images

Classification Reports for Chest X-rays: VGG, InceptionV3, ResNet50, Xception

Confusion Matrix for Chest X-rays: VGG, InceptionV3, ResNet50, Xception

Classification Reports for CT Scans: VGG, InceptionV3, ResNet50, Xception

Confusion Matrix for CT Scans: VGG, InceptionV3, ResNet50, Xception

Screenshots of Flask App

For more screenshots, please visit the screenshots folder of my repo, or click here

How to use Flask App

• Download repo, change to directory of repo, go to command prompt and run pip install -r requirements.txt
• On command prompt, run python app.py
• Open your web browser and go to 127.0.0.1:5000 to access the Flask App

How to use Jupyter Notebooks

• Download my repo and upload the repo folder to your Google Drive
• Go to the jupyter notebooks folder in my repo, right click the notebook you want to open and select Open with Google Colab