Early Paediatric Pneumonia diagnosis

A comparative study between a pre-trained model (VGG-19) and a custom CNN model (from scratch) for early pneumonia diagnosis among children

Table of Contents

1. Introduction
2. Methodology Transfer Learning with VGG-19 Custom CNN Model
3. Dataset
4. Results

Introduction

This project aims to classify pediatric pneumonia from chest X-rays using deep learning techniques. Two approaches were explored: transfer learning with the pre-trained VGG-19 model and a custom-designed CNN model.

Methodology

Transfer Learning with VGG-19

Model Preparation: The pre-trained VGG-19 model was used, with its fully connected layers removed to preserve the convolutional bases. A custom dense layer with softmax activation was appended to serve as the output layer for binary classification. All layers of the VGG-19 model, except the newly added dense layer, were frozen to retain pre-learned features and enable effective class distinction. Model Compilation: Optimizer: Adam with a learning rate of 1e-3. Loss Function: Binary cross-entropy. Evaluation Metric: Accuracy. Training: The model was trained using a dataset with images resized to 224x224 pixels. Performance was validated using a validation set to ensure generalization and prevent overfitting.

Custom CNN Model

Data Preparation: Image data augmentation techniques (rotation, shifting, zooming, shearing, horizontal flipping) were applied to enhance generalization. Model Architecture: The custom CNN consisted of multiple convolutional layers with max-pooling layers to reduce spatial dimensions and extract hierarchical features. ReLU activation was used to introduce non-linearity. Batch normalization was applied for model stability and faster convergence. Two fully connected layers with a 20% dropout rate were included to address overfitting. The output layer had two nodes with softmax activation for binary classification. Model Compilation: Optimizer: Stochastic Gradient Descent (SGD). Loss Function: Binary cross-entropy. Training: The model was trained over multiple epochs, adjusting weights and biases through backpropagation. Validation data was used to assess model performance during training.

Dataset

The dataset comprises 8,287 JPEG images of pediatric chest X-rays collected at Guangzhou Women and Children's Medical Center, China. The dataset includes:

Training Set:

Normal: 805 images Pneumonia: 3,406 images

Test Set:

Normal: 234 images Pneumonia: 390 images

Validation Set:

Normal: 536 images Pneumonia: 2,916 images The images were resized to 256x256 pixels, and expert physicians assigned diagnostic labels to ensure accuracy.

Results

The project successfully implemented transfer learning with VGG-19 and a custom CNN model to classify pediatric pneumonia from chest X-rays. Technically, custom CNN gave a better performance accuracy and also, it was comparatively faster and less complex than the pre-trained VGG 19 model