Objective: To perform image classification to discern normal versus COVID-19 infected lungs based on X-ray images.

Datasource: https://www.kaggle.com/datasets/nabeelsajid917/covid-19-x-ray-10000-images

Analysis: We created a deep learning framework to perform image classification. We relied heavily on the Keras python library to pre-process the data and develop our training model. After loading the images, we were able to see how different COVID-19 infected lungs looked as compared to normal ones (see below).

Model Training: Keras offers two different ways of defining a network. We will use the Sequential API, where you just add on one layer at a time, starting from the input. Notably, the convolutional layers had 16-32 filters using nine weights each to transform a pixel to a weighted average of itself and its eight neighbors. After this is applied to the entire image, max pooling just looks at the nearest four neighbors and selects the maximal value. In addition, we applied batch normalization and dropout as a regularization method.

One epoch is when an entire dataset is passed forward and backward through the neural network only once. While the batch size is the total number of training examples present in a single batch.

Output: We were able to successfully classify normal and COVID-19 infected lungs using X-ray images.