medicalai 1.0.6

Medical-AI is a AI framework specifically for Medical Applications

medicalAI

Medical-AI is a AI framework specifically for Medical Applications

Installation

pip install medicalai

Requirements

Python Version : 3.5-3.7 (Doesn't Work on 3.8 Since Tensorflow does not support 3.8 yet.

Dependencies: Numpy, Tensorflow, Seaborn, Matplotlib, Pandas

NOTE: Dependency libraries are automatically installed. No need for user to install them manually.

Usage

Importing the Library

import medicalai as mai

Using Templates

You can use the following templates to perform specific Tasks

Load Dataset From Folder

Set the path of the dataset and set the target dimension of image that will be input to AI network.

trainSet,testSet,labelNames =mai.datasetFromFolder(datasetFolderPath, targetDim = (96,96)).load_dataset()

- trainSet contains 'data' and 'labels' accessible by trainSet.data and trainSet.labels
- testSet contains 'data' and 'labels' accessible by testSet.data and testSet.labels
- labelNames contains class names/labels

Check Loaded Dataset Size

print(trainSet.data.shape)
print(trainSet.labels.shape)

Run Training and Save Model

trainer = mai.TRAIN_ENGINE()
trainer.train_and_save_model(AI_NAME= 'tinyMedNet', MODEL_SAVE_NAME='PATH_WHERE_MODEL_IS_SAVED_TO', trainSet, testSet, OUTPUT_CLASSES, RETRAIN_MODEL= True, BATCH_SIZE= 32, EPOCHS= 10, LEARNING_RATE= 0.001)

Plot Training Loss and Accuracy

trainer.plot_train_acc_loss()

Plot Confusion matrix of test data

trainer.plot_confusion_matrix(labelNames,title='Confusion Matrix of Trained Model on Test Dataset')

Loading Model for Prediction

model = mai.load_model_and_weights(modelName = 'PATH_WHERE_MODEL_IS_SAVED_TO')

Predict With Labels

mai.predict_labels(model, testSet.data[0:2], expected_output =testSet.labels[0:2], labelNames=labels, top_preds=3)

Get Just Values of Prediction without postprocessing

model.predict(testSet.data[0:2])

Advanced Usage

Code snippet for Training Using Medical-AI

## Setup AI Model Manager with required AI. 
model = mai.modelManager(AI_NAME= AI_NAME, modelName = MODEL_SAVE_NAME, x_train = train_data, OUTPUT_CLASSES = OUTPUT_CLASSES, RETRAIN_MODEL= RETRAIN_MODEL)

# Start Training
result = mai.train(model, train_data, train_labels, BATCH_SIZE, EPOCHS, LEARNING_RATE, validation_data=(test_data, test_labels), callbacks=['tensorboard'])

# Evaluate Trained Model on Test Data
model.evaluate(test_data, test_labels)

# Plot Accuracy vs Loss for Training
mai.plot_training_metrics(result)

#Save the Trained Model
mai.save_model_and_weights(model, outputName= MODEL_SAVE_NAME)