Project 9: Image Classification using Deep Learning

Project : Classifying Covid-19 positive and negative patients from X-ray images

About the Project: The outbreak of COVID-19 has had an immense impact on world health and daily life in many countries. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 infections in the patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays.

This project uses radiological imaging to determine whether the scanned patient has COVID-19 or not.

Aim: With the Chest X - Ray dataset, develop a Deep Learning Model to classify the X Rays of Healthy vs Corona positive patients.

Objectives/Exercises:

1. Import the dataset in python Notebook
2. Explore the dataframe
3. Perform data transformation to preprocess the images to convert the images to the same size and greyscale.
4. Perform normalization techniques on the images
5. Split the dataset into training and testing sets.
6. Create a Convolution Neural Network (CNN) model to classify the images into positive and negative COVID-19 infections.
7. Test the CNN model and critically evaluate the performance of the model

Tools & libraries to Use: JupyterNotebook or Google Colab, Python3, Numpy, OpenCV, Tensorflow, Keras etc

DataSet

1. Create Folder 'DataSet'

2. Fetch images from Covid Nagative Images and save them under subfolder 'Covid19 Negative'

3. Fetch images from Covid Positive Images and save them under subfolder 'Covid19 Positive'