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Glaucoma Detection Using Deep Learning

Glaucoma is a leading cause of irreversible blindness worldwide, making early detection crucial to prevent vision loss. Our project, "Glaucoma Detection Using Deep Learning," uses advanced AI techniques to identify glaucoma at an early stage. By training a model on a dataset of retinal images, we can distinguish between healthy and glaucomatous eyes with high accuracy. This approach helps ophthalmologists diagnose glaucoma more efficiently, potentially saving millions from vision impairment. We use state-of-the-art algorithms like convolutional neural networks (CNNs) to analyze retinal images. To enhance image quality, we preprocess the data and apply augmentation techniques to make the model more robust. The trained model is evaluated on various metrics to ensure it meets clinical standards for reliability and precision. This project demonstrates the power of deep learning in medical diagnostics and contributes to the global effort to combat glaucoma.

Explanation All Code

Step 1:

Mounting Google Drive

We mount Google Drive to access our dataset stored in the cloud.


Import and install the necessary packages.

Importing Libraries

We import essential libraries for data processing, model building, evaluation, and visualization.


Data collection and preparation:


We utilized the Glaucoma Dataset for Deep Learning Classification. We collected a Glaucoma dataset consisting of 1800 images. After augmenting the images, the total dataset increased to 3000 images. Then, divide the dataset into 80% for training and 20% for validation.


Load Datasets

We set the paths for training and validation datasets stored in Google Drive.


Listing Categories

We list the categories (labels) in the training dataset directory.


Data Processing

We define a function to read and resize images, storing them with their respective labels. We also count the number of images per class.


Processing Training Data

We process the training data and print the total number of training samples.


Plotting Class Distributions

We plot the distribution of classes in the training dataset.


Processing Validation Data

We process the validation data and print the total number of validation samples.


Plotting Validation Data Distribution

We plot the distribution of classes in the validation dataset.


Step 2:

Preparing Data for Model

We convert image data and labels to numpy arrays and normalize the images.


Visualizing Random Training Images

We display random images from the training dataset for visualization.


Step 3:

Glaucoma Detection Using Vision Transformer Model, Custom CNN Model, and VGG16 Model.

  • Vision Transformer Model: This model leverages the power of transformers for image recognition tasks. It is highly effective in capturing long-range dependencies in images, making it suitable for detecting glaucoma.
  • Custom CNN (Convolutional Neural Network) Model: This model is specifically designed for this project, allowing customization to better recognize patterns in eye images that indicate glaucoma.
  • VGG16 Model: This is a well-known Deep learning model that is effective in image classification tasks. Its 16-layer architecture helps in detailed image analysis, which is crucial for detecting subtle signs of glaucoma.


By using these models, my project aims to accurately detect glaucoma from medical images, leveraging the strengths of each model to improve overall performance.

Build a Vision Transformer Model

We define and compile a Vision Transformer Model.


Training the Vision Transformer Model.

We train the model and save the best weights.


Save the Model

Plotting Training History

We plot the accuracy and loss curves of the model.


Evaluating Model Performance

Evaluating the Model

We load the best model and evaluate it on the test data. There is the accuracy of 62.17%


Plotting Confusion Matrix and Classification Report

We plot the confusion matrix and print the classification report.


Step 4:

Build Custom CNN Model 

We define and compile a Custom CNN Model.


Training the Basic CNN Model

We train the model and save the best weights.


Plotting Training History

We plot the accuracy and loss curves of the model.


Evaluating Model Performance

Evaluating the Model

We load the best model and evaluate it on the test data. There is the accuracy of 89.33%.


Plotting Confusion Matrix and Classification Report

We plot the confusion matrix and print the classification report.


Step 5:

Building a Vgg16 model

We define and compile a Vgg16 model.


Training the Vgg16 model

We train the model and save the best weights.


Plotting Training History

We plot the accuracy and loss curves of the model.


Evaluating Model Performance

Evaluating the Model

We load the best model and evaluate it on the test data. There is the VGG16 Accuracy of 80.50%.


Plotting Confusion Matrix and Classification Report

We plot the confusion matrix and print the classification report.


Step 6:

Prediction

Load The Training Model

Conclusion


Our project, "Glaucoma Detection Using Deep Learning," demonstrates the powerful capabilities of Deep learning in medical diagnostics. By effectively identifying glaucoma from retinal images, our model shows promise in aiding early detection and treatment, potentially reducing the global burden of this debilitating disease. The high accuracy and reliability of our model underscore the potential for integrating such technology into clinical practice, providing ophthalmologists with a valuable tool to enhance patient care. Through rigorous testing and validation, we have ensured that our model performs consistently and meets the required standards for medical diagnostics. Our work highlights the importance of interdisciplinary collaboration, combining expertise in ophthalmology and Deep learning to address critical health challenges. Moving forward, we aim to further refine our model, explore its application to other eye diseases, and work towards its adoption in healthcare settings. This project represents a significant step towards leveraging technology to improve healthcare outcomes and quality of life for millions of people worldwide.

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