COVID-19 Image Classification using Deep Learning Techniques

Introduction

This repository contains the code and data for a research project focused on using deep learning techniques to classify COVID-19 X-ray and CT-Scan images. The goal of the project was to create a model that could accurately differentiate COVID-19 cases from normal cases and other negative cases. The research paper is available in the paper folder.

This is a project with a working website integrated with a CNN model to make predictions whether a patient is covid-19 positive or not. (http://13.233.123.254:8080/)

Requirements

• Python 3.8
• TensorFlow 2.2.0
• NumPy
• OpenCV
• Matplotlib
• Flask

Dataset

The datasets used for this project are:

• COVID-19 CT-SCAN Database. This dataset contains CT-scan images of COVID-19 cases, and normal cases.

Architecture

Architecture of Binary Classifier

Code

The code folder contains the code for training and evaluating the model. There is one notebook:

• covid-19 analysis using transfer learning.ipynb: Preprocesses the dataset, prepares it for training and evaluates the models.

Results

The trained model achieved an accuracy of 96.15% on the test set. The confusion matrix is shown below:

	COVID-19	Normal
COVID-19	0.96	0.04
Normal	0.04	0.96

Conclusion

The results of this research project show that deep learning techniques can be effective in classifying COVID-19 cases from normal cases and other pneumonia cases using X-ray images.

The DenseNet201 model with modified head converged to classifiy the prositive and the negative cases of COVID19 with 96% accuracy, 96% recall (macro avg) and 96% precision (macro avg).

Citation

If you use this code or dataset in your research, please cite our paper:

• Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Relevant Papers

• Rapid COVID‑19 diagnosis using ensemble deep transfer learning models from chest radiographic images