eastk1m / aegle-ai

Aegle-AI

https://www.nature.com/articles/s43856-022-00199-0

Creating protype programs using artificial intelligence to help diagnose diseases such as cardiovascular disease or breast cancer.

Methodology: convolutional neural networks (CNN), machine learning, artificial intelligence, python, tensorflow, etc.

Convolutional Neural Network (CNN) for EKG classification and mammogram scans using Python and TensorFlow/Keras.

1. Data Preparation:

   • Data Sets
   • Preprocess the data (filtering, normalization, and segmentation into individual heartbeats).

2. Feature Extraction:

   • Extract relevant features from each ECG segment (e.g., QRS complexes, ST segments).
   • Convert the ECG signal into a suitable format (e.g., 1D time-series).

3. Model Architecture:

   • Build a CNN model using Keras:

     import tensorflow as tf
     from tensorflow.keras.models import Sequential
     from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout
     
     model = Sequential()
     model.add(Conv1D(filters=32, kernel_size=5, activation='relu', input_shape=(num_features, 1)))
     model.add(MaxPooling1D(pool_size=2))
     model.add(Flatten())
     model.add(Dense(64, activation='relu'))
     model.add(Dropout(0.5))
     model.add(Dense(1, activation='sigmoid'))  # Binary classification

4. Compile and Train:

   • Compile the model:

     model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

   • Train the model on your preprocessed dataset:

     model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))

5. Evaluation:

   • Evaluate the model using validation or test data:

     loss, accuracy = model.evaluate(X_test, y_test)
     print(f"Test accuracy: {accuracy:.4f}")

6. Deployment:

   • Deploy the trained model in a clinical setting (consult with domain experts).
   • Monitor its performance and update as needed.