Classify-X-rays-using-CNN

The dataset contains chest x-ray images of Covid-19, Pneumonia and normal patients can be extracted from https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia

The objectives of this CNN project is practice how to apply convolutional layer in image classification project. I trained three different model and compare their performance with accuracy and confusion matrix

• Model 1: Using input, flatten and output layer to train model.
• Model 2: Using input, two convolutional layer, two MaxPooling2D layer, two dropout layer, flatten layer and output to train model.
• Model 3: Using ResNet50V2 as base model, it consists of 50 layers, including convolutional layers, pooling layers, and fully connected layers.

Based on 20 epoch, 0.001 learning rate, Adam optimier, CategoricalCrossentropy loss function and CategoricalAccuracy metrics.

	Training Accuracy	Validation Accuraccy
Model 1	78.8103 %	86.1801 %
Model 2	88.1998 %	92.3913 %
Model 3	95.3149 %	92.0031 %

Model Loss, Confusion Matrix

Model 1:

Model 2:

Model 3:

From the confusion matrix result, the accuracy of classification for Covid-19 and Normal will lower than Penumonia since the dataset was imbalanced and most of the images are Pneumonia.