Merge pull request #370 from SayantikaLaskar/eyedisease

Add Eye disease classification

Eye Disease Classification/Dataset/README.md

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+The link to the dataset:
+https://www.kaggle.com/datasets/kondwani/eye-disease-dataset

Eye Disease Classification/README.md

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+# Eye Disease Classification Using CNN, VGG16, and ResNet-50
+
+## Overview
+
+This repository contains the implementation of an eye disease classification system using Convolutional Neural Networks (CNN), VGG16, and ResNet-50 architectures. The project aims to accurately classify various eye diseases from retinal images.
+
+This project is part of the GirlScript Summer of Code 2024 program.
+
+## Table of Contents
+- [Overview](#overview)
+- [Table of Contents](#table-of-contents)
+- [Features](#features)
+- [Dataset](#dataset)
+- [Requirements](#requirements)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Architecture](#model-architecture)
+- [Training](#training)
+- [License](#license)
+
+## Features
+- Data preprocessing and augmentation.
+- Implementation of CNN, VGG16, and ResNet-50 models.
+- Training and validation scripts.
+
+## Dataset
+The dataset used for this project consists of retinal images labeled with various eye diseases. You can download the dataset from [Kaggle Eye Disease Dataset](https://www.kaggle.com/datasets/kondwani/eye-disease-dataset).
+
+## Requirements
+- Python 3.7+
+- TensorFlow 2.x
+- Keras
+- NumPy
+- Pandas
+- Matplotlib
+- scikit-learn
+
+## Installation
+1. Clone the repository:
+    ```bash
+    git clone https://github.com/yourusername/eye-disease-classification.git
+    cd eye-disease-classification
+    ```
+
+2. Install the required packages:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Usage
+1. **Preprocess the Data:**
+    - Download the dataset and place it in the `data/` directory.
+    - Run the preprocessing script to prepare the data for training:
+        ```bash
+        python preprocess.py
+        ```
+
+2. **Train the Model:**
+    - Train the CNN model:
+        ```bash
+        python train.py --model cnn
+        ```
+    - Train the VGG16 model:
+        ```bash
+        python train.py --model vgg16
+        ```
+    - Train the ResNet-50 model:
+        ```bash
+        python train.py --model resnet50
+        ```
+
+3. **Evaluate the Model:**
+    - Evaluate the trained model:
+        ```bash
+        python evaluate.py --model model_name
+        ```
+
+## Model Architecture
+### Convolutional Neural Network (CNN)
+A custom CNN architecture designed for image classification.
+
+### VGG16
+A pre-trained VGG16 model fine-tuned for eye disease classification.
+
+### ResNet-50
+A pre-trained ResNet-50 model fine-tuned for eye disease classification.
+
+## Training
+The training process involves:
+- Splitting the dataset into training, validation, and test sets.
+- Data augmentation to improve model generalization.
+- Training the model with early stopping and model checkpointing.
+
+
+## License
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

Eye Disease Classification/Requirements.txt

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+**Requirements For Project :-**
+
+1. NumPy: Fundamental package for numerical computing.
+2. pandas: Data analysis and manipulation library.
+3. scikit-learn: Machine learning library for classification, regression, and clustering.
+4.  Matplotlib: Plotting library for creating visualizations.
+5.  Keras: High-level neural networks API, typically used with TensorFlow backend.
+6. seaborn: Statistical data visualization library based on Matplotlib.