Chest X-ray images are commonly used in the med- ical field to diagnose respiratory conditions such

as COVID-19, pneumonia, and lung opacity (Non- COVID lung infection). However, interpreting these

images accurately can be challenging even for trained professionals. This is where machine learning comes in - by training a model to classify these images, we can

potentially improve the accuracy and efficiency of di- agnosis. In this project, we aim to classify chest X-ray

images into four categories: COVID-19, normal, lung opacity, and viral pneumonia. To accomplish this, we will use convolutional neural networks (CNNs), which are a type of deep learning model commonly used for

image classification tasks. CNNs are particularly well- suited for image classification tasks because they can

automatically learn and extract relevant features from images, making them more effective than traditional machine learning models for this type of data. 3 Data Preparation We used a publicly available dataset from Kaggle [https://www.kaggle.com/datasets/tawsifurrahman/ covid19-radiography-database] that consists of 21165 chest X-ray images . The dataset is split into four categories: COVID19 (3 616 images), normal (10,192 images), lung opacity( 6012 images), and viral pneumonia (1345 images). For the first Model , data preparation involved loading the images from the file system into memory and resizing them to a fixed size of 100x100 pixels. The images were then converted to arrays and normalized to have values between 0 and 1. Finally, the data was split into training and validation sets, with 20 for the second Model, data preparation was done using a data generator, which loaded images on the fly from the file system and preprocessed them using the VGG16 preprocessor. The images were resized to a fixed size of 224x224 pixels, which is the input size required by the VGG16 model. The data generator also performed data augmentation by randomly applying horizontal flips and rotations to the images. The data was split into training and validation sets using the validation split parameter of the flow from directory method.

The data preparation process for the second model is more efficient than the first one because it loads images on the fly instead of loading them all into memory at once. This allows for the processing of larger datasets that may not fit in memory. Additionally, data aug- mentation helps to prevent overfitting and improves the model’s ability to generalize to new data