Glaucoma Detection Using Deep Learning

Project Overview

Glaucoma Detection Using Deep Learning is an application of using advanced neural networks to categorize images of retinal images as either glaucoma positive" or "glaucoma negative. The primary aim is to develop a high-accuracy diagnostic machine for glaucoma detection that uses convolutional neural networks (CNN), Vision Transformer model, and VGG 16 models to help ophthalmologists in the early stages of glaucoma detection.

This project focuses on the stepwise approach to automating glaucoma detection by training deep-learning models on medical images. If AI in the medical field interests you, then this project will demonstrate the impact of machine learning where lives can be saved spoiling the odds of blindness.

Prerequisites

Before we jump into the code, here's what you'll need:

• An understanding of Python programming and usage of Google Colab

• Basic knowledge about deep learning and medical images.

• Comfortable using frameworks like Tensorflow, Keras, Numpy, OpenCV, and Seaborn to handle data and build models and visualize data and performance of models

• A training and a testing set of retinal images.

Once you organize these tools, we assure you that you will notice how almost all of them can be used in the following step. Also, do not stress if you are not a Python master through the tutorial, you will understand every line of the code!

Approach

The approach for this work consists of developing several deep learning techniques (Vision Transformers, Custom CNN, VGG16), followed by the assessment and visualization of the findings. The major steps involve:

• Obtaining and preparing data (augmentation, resizing, normalizing)

• Training and measuring the performance of several architectures

• Visualizing performance with confusion matrices and accuracy plots

Workflow and Methodology

This project can be divided into the following basic steps:

• Data Collection: We collected the retinal dataset labeled glaucoma positive or negative from Kaggle.

• Data preprocess: To improve the model performance and achieve higher accuracy, we applied different preprocessing techniques. First, we augmented the dataset to create a balanced dataset. Then we resized and normalized the images in 0 to 1 pixel values.

• Model Selection: In this project, there are three models used (Vision Transformer, Custom CNN, and VGG16).

• Training and Testing: Each of the models has been trained on the preprocessed dataset and later, tested on the dataset that was not used during training.

• Model Evaluation: The evaluation of the model's performance is done by evaluating accuracy, precision, recall, confusion matrix, etc.

The methodology includes

• Data Preprocessing: The images are resized, normalized, and augmented to improve the performance of models based on them.

• Model Training: Each model is trained with 100 epochs to enhance the level of performance.

• Evaluation: Standard metrics (accuracy, working of confusion matrix) are applied to assess the efficiency of the models.

Data Collection

We collected a dataset containing 1800 retinal images with both glaucoma-positive and glaucoma-negative cases from Kaggle. After data augmentation, images were increased to 3000 images. 80% set aside for training, while 20% for validation.

Data Preparation

Data Preparation Workflow

Resizing Images: All the images were adjusted to a size of 128x128 pixels to ensure uniformity in the input to the model.

Augmentation: Rotation, flipping, and changes in contrast, among others, are employed to increase the diversity of the datasets.