⚡️Eye Dataset Classification with TensorFlow and Keras 🚀

This project showcases the implementation of a Convolutional Neural Network (CNN) to classify eye images into predefined categories. The primary focus is to demonstrate the end-to-end workflow of image classification, starting from dataset preparation to training, evaluation, and visualization of results.

Project Overview

Eye classification is an essential task in medical image analysis and computer vision. This project builds a deep learning model capable of identifying specific categories of eye images from a dataset. Using TensorFlow and Keras, the model leverages CNN layers to extract features, classify images, and deliver accurate predictions.

Key Steps in the Project

1. Dataset Preparation 📊

The dataset is assumed to be organized into subfolders, each corresponding to a specific class. The project utilizes the following steps to prepare the dataset:

• Image Resizing: All images are resized to 200x200 pixels to ensure uniformity and compatibility with the model.
• Data Normalization: Pixel values are rescaled to the range [0, 1] for faster convergence during training.
• Data Splitting: The ImageDataGenerator class splits the dataset into training (90%) and validation (10%) subsets using its validation_split parameter.

2. Model Architecture 🏛️

The CNN model is designed using the Sequential API, which allows layers to be stacked in order. The architecture consists of:

• Convolutional Layers: Extract spatial features from input images using 32, 64, and 128 filters.

• MaxPooling Layers: Reduce the dimensions of feature maps, minimizing computational cost and retaining critical information.

• Dropout Layer: Introduced after dense layers to reduce overfitting by randomly disabling neurons during training.

• Dense Layers: Fully connected layers are added for classification. The final layer uses the softmax activation function to output class probabilities for the 4 categories.

  
  

3. Model Compilation ✅

The model is compiled with the following configurations:

• Optimizer: Adam, known for its efficient and adaptive learning rate.

• Loss Function: categorical_crossentropy, suitable for multi-class classification problems.

• Metric: accuracy, used to monitor the model's performance during training and validation.

  
  

4. Model Training 🏃🏽

The model is trained using the prepared training dataset for 10 epochs. During training:

• The model learns to recognize patterns in images using the convolutional layers.

• Validation data is used to monitor performance and prevent overfitting.

  
  

5. Evaluation and Visualization 👁️

The model's performance is evaluated using several methods:

• Accuracy and Loss Graphs: Training and validation accuracy and loss are plotted to visualize the model's learning curve.

• Predictions: Sample predictions are generated using the test dataset, displaying the true labels alongside the predicted labels.

  
  

6. Examples for Project ⚡️

Where is Datasets ?

I use to kaggle datasets for my project. This program is has big data. Examples health, machine, computer science...

Visualizations 👁️

1. Training Metrics:
   • Accuracy and loss plots provide insights into the model's learning behavior over epochs.
   • Validation curves highlight the model's generalization ability to unseen data.
2. Sample Predictions:
   • A set of 5 images from the test dataset is visualized, showing the true and predicted class labels. This provides a clear understanding of the model's practical performance.

How to Use This Project 🤨

1. Setup Dataset: Ensure the dataset is structured as follows:

   Eye dataset/
       ├── Class1/
       ├── Class2/
       ├── Class3/
       └── Class4/
   

   Replace Class1, Class2, etc., with actual class names.

3. Install Dependencies: Install the required Python libraries:

   pip install tensorflow matplotlib numpy

3. Update the Dataset Path: Modify the data_dir variable in the code to point to your dataset's location.

4. Run the Code: Execute the Python script to train the model, generate evaluation plots, and display predictions.

Results and Insights

• The model achieves promising accuracy, demonstrating its ability to classify eye images into multiple categories.

• Visualization of training metrics and sample predictions helps understand the model's strengths and areas for potential improvement.

  
  

Future Work 🪐

• Experiment with advanced data augmentation techniques to improve model generalization.
• Explore deeper architectures or pre-trained models (e.g., VGG16, ResNet) for enhanced accuracy.
• Evaluate the model on additional datasets or real-world data for broader applicability.

This project provides a foundational understanding of building CNN models for image classification tasks, enabling further exploration and improvements in medical imaging applications.

Board of Library

Technology	Where ?
TensorFlow	Deep Learning
Keras	Deep Learning
Matlpotlib	Graph Learning
ImageDataGenerator	Data Learning

Library 📚📒📕📙