Work Required in Heart Sound Classification (heartclassification-main) Project

[papichoolo]

Hello
I have created a Neural Network Implementation for Classifying Different Heartbeat related conditions. In this folder we used two methods, CNN to classify the spectrogram images of the Heart sound, and RNNs to classify the raw audio data.
You can contribute to the project in the following ways:

• Implement more robust state-of-the-art CNN methods (currently using a basic functional CNN model from keras)
• Using Better ways to capture raw audio data or use better RNN methods (currently using Bi-directional LSTM)

You can start by using ResNet or EfficientNet model as CNN model.
Happy coding!

[TechieSamosa]

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That sounds like a fascinating project! Here are some specific suggestions and steps you can take to implement improvements:

Improving the CNN Model:

• Transfer Learning with ResNet or EfficientNet:

  • Implement a CNN using pre-trained models like ResNet or EfficientNet. These models have shown excellent performance in various computer vision tasks.

• Architecture Modifications:

  • Experiment with different CNN architectures, such as DenseNet or Inception, to see if they perform better on your heartbeat classification task.

• Data Augmentation:

  • Apply data augmentation techniques like rotation, zooming, and flipping to augment your dataset. This can help the model generalize better.

• Hyperparameter Tuning:

  • Experiment with different learning rates, batch sizes, and optimizers. Grid search or random search can be helpful.

• Regularization Techniques:

  • Use techniques like dropout, L2 regularization, or batch normalization to prevent overfitting.

• Class Imbalance Handling:

  • If there's a class imbalance, consider using techniques like oversampling, undersampling, or using class weights during training.

Improving the RNN Model:

• Bi-directional LSTM:

  • You're already using a Bi-directional LSTM, which is a good choice. Consider adjusting the number of layers and units to see if performance improves.

• GRU or Other RNN Variants:

  • Experiment with other RNN variants like Gated Recurrent Units (GRU) or even more advanced variants like Transformer models.

• Attention Mechanisms:

  • Implement attention mechanisms to help the model focus on the most relevant parts of the audio data.

• Combining CNN and RNN:

  • You could explore architectures that combine CNNs for spectrogram analysis and RNNs for raw audio data. This can capture both time and frequency domain information.

• Feature Engineering:

  • Consider extracting additional features from the raw audio data (e.g., MFCC features) and use them as inputs to your RNN.

General Project Improvements:

• Code Organization:

  • Ensure that your code is well-organized, with clear separation of concerns (e.g., separate files for data preprocessing, model definitions, and training scripts).

• Documentation and Comments:

  • Provide detailed comments for complex operations or logic. Document your code for yourself and others who may work on the project.

• Logging and Monitoring:

  • Implement logging to keep track of experiments and model performance over time. You could use tools like TensorBoard.

• Unit Testing:

  • Write unit tests to ensure that different components of your codebase are functioning correctly.

• Model Evaluation:

  • Use appropriate metrics (accuracy, precision, recall, F1-score, etc.) to evaluate your models. Consider using cross-validation for robustness.

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Happy coding! 🚀