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.
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Data Preparation:
- Data Sets
- Preprocess the data (filtering, normalization, and segmentation into individual heartbeats).
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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).
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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
- Build a CNN model using Keras:
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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))
- Compile the model:
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Evaluation:
- Evaluate the model using validation or test data:
loss, accuracy = model.evaluate(X_test, y_test) print(f"Test accuracy: {accuracy:.4f}")
- Evaluate the model using validation or test data:
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Deployment:
- Deploy the trained model in a clinical setting (consult with domain experts).
- Monitor its performance and update as needed.