Deep Learning Project Development Environment Setup Guide
[x] ## 1. Data Collection:
Requirements:
A diverse dataset of ocular images (cataract and non-cataract).
Properly labeled images for supervised learning.
Tools:
Image collection tools (e.g., digital cameras, medical imaging devices).
Image labeling tools (e.g., LabelImg, RectLabel).
[x] ## 2. Data Preprocessing:
Requirements:
Python with data science libraries (NumPy, Pandas).
Image processing libraries (OpenCV).
Deep learning libraries (TensorFlow, Keras).
Tools:
Jupyter Notebooks for interactive development.
Steps:
[x] 1. Resize and Normalize Images:
Use OpenCV or other libraries to resize images to a standardized format (e.g., 224x224 pixels).
Normalize pixel values for better convergence during training.
[x] 2. Data Augmentation:
Apply data augmentation techniques (rotation, flipping, zooming) to increase dataset diversity.
Python libraries like TensorFlow and Keras offer built-in functions for data augmentation.
[x] 3. Dataset Splitting:
Split the dataset into training, validation, and testing sets (e.g., 80%, 10%, 10%).
[x] ## 3. Model Selection and Design:
Requirements:
Python with TensorFlow and Keras.
Tools:
Jupyter Notebooks or any Python IDE.
Steps:
[x] 1. Choose a Model Architecture:
Select a deep learning architecture suitable for image classification (e.g., CNN).
Popular architectures include VGG, ResNet, or you can design a custom architecture.
[x] 2. Model Design:
Design the architecture, considering layers, activation functions, dropout for regularization.
Use TensorFlow and Keras to implement the model.
[x] 3. Output Layer Configuration:
Configure the output layer with one node for binary classification (cataract or non-cataract).
Use activation functions like sigmoid for binary classification.
4. Experimental Setup:
Requirements:
A computer with decent specifications (CPU and GPU).
Python with necessary libraries installed.
Tools:
Python environment (Anaconda is recommended for managing dependencies).
Steps:
Setup Python Environment:
Install Python and required libraries using virtual environments or Anaconda.
Install Libraries:
Install TensorFlow, Keras, OpenCV, NumPy, and other necessary libraries.
[x] ## 5. Training and Evaluation:
Requirements:
GPU (optional but recommended for faster training).
Tools:
Jupyter Notebooks or Python scripts.
Steps:
[x] 1. Train the Model:
Use the training set to train the deep learning model.
[x] 2. Evaluate the Model:
Use the validation set to evaluate the model's performance using metrics like accuracy, precision, recall, and F1 score.
[x] ## 7. Comparison with Other Models:
Tools:
Python scripts for metrics calculation.
Steps:
Implement Metric Calculation:
Calculate metrics for your model and other established models.
Compare Results:
Analyze and compare the performance of different models.
[x] ## 8. Fine-tuning and Optimization:
Steps:
Experiment with Hyperparameters:
Fine-tune hyperparameters like learning rate, batch size, etc.
Optimize Model:
Optimize the model architecture based on experimentation results.
[x] ## 9. Results Analysis:
Tools:
Python scripts for visualization.
Steps:
Visualize Results:
Visualize results using confusion matrices, accuracy, and loss graphs.
[x] ## 10. 4. Development of Mobile Application
Steps:
Development:
[x] - Choose either React Native or Flutter for cross-platform mobile app development.
[x] - Set up the development environment for React Native or Flutter
[x] - Implement the user interface (UI) for the mobile app, including screens for image capture and classification results
[x] - Integrate the trained deep learning model into the mobile app using platform-specific libraries or packages
[x] - Develop features for capturing ocular images using the device's camera
[x] - Preprocess captured images and pass them to the deep learning model for inference
[x] - Ensure smooth integration between the model and the mobile app interface, providing user-friendly feedback on classification results
[ ] - Test the mobile app extensively on different devices and platforms to ensure compatibility and performance
[ ] 2. Deployment:
[x] - Depending on the model size and computational requirements, deploy the model directly within the mobile app
[x] - Consider cloud deployment if necessary, especially for larger models or resource-intensive computations
[ ] - Deploy the mobile app to the respective app stores (Google Play Store for Android, Apple App Store for iOS) using the deployment tools provided by React Native or Flutter
[ ] - Monitor app performance and user feedback post-deployment, and update the app as needed as we pre[are for panel show case
Deep Learning Project Development Environment Setup Guide
Requirements:
Tools:
Image collection tools (e.g., digital cameras, medical imaging devices).
Image labeling tools (e.g., LabelImg, RectLabel).
[x] ## 2. Data Preprocessing:
Requirements:
Tools:
Steps:
[x] 1. Resize and Normalize Images:
[x] 2. Data Augmentation:
[x] 3. Dataset Splitting:
[x] ## 3. Model Selection and Design:
Requirements:
Tools:
Steps:
[x] 1. Choose a Model Architecture:
[x] 2. Model Design:
[x] 3. Output Layer Configuration:
4. Experimental Setup:
Requirements:
Tools:
Steps:
Setup Python Environment:
Install Libraries:
Requirements:
Tools:
Steps:
[x] 1. Train the Model:
[x] 2. Evaluate the Model:
[x] ## 7. Comparison with Other Models:
Tools:
Steps:
Implement Metric Calculation:
Compare Results:
Steps:
Experiment with Hyperparameters:
Optimize Model:
Tools:
Steps:
Steps:
[x] - Choose either React Native or Flutter for cross-platform mobile app development.
[x] - Set up the development environment for React Native or Flutter
[x] - Implement the user interface (UI) for the mobile app, including screens for image capture and classification results
[x] - Integrate the trained deep learning model into the mobile app using platform-specific libraries or packages
[x] - Develop features for capturing ocular images using the device's camera
[x] - Preprocess captured images and pass them to the deep learning model for inference
[x] - Ensure smooth integration between the model and the mobile app interface, providing user-friendly feedback on classification results
[ ] - Test the mobile app extensively on different devices and platforms to ensure compatibility and performance
[ ] 2. Deployment:
[x] - Depending on the model size and computational requirements, deploy the model directly within the mobile app
[x] - Consider cloud deployment if necessary, especially for larger models or resource-intensive computations
[ ] - Deploy the mobile app to the respective app stores (Google Play Store for Android, Apple App Store for iOS) using the deployment tools provided by React Native or Flutter
[ ] - Monitor app performance and user feedback post-deployment, and update the app as needed as we pre[are for panel show case