Metadata-Version: 2.1
Name: medicalai
Version: 1.1.44
Summary: Medical-AI is a AI framework specifically for Medical Applications
Home-page: https://github.com/aibharata/medicalAI
Author: Vinayaka Jyothi - For AiBharata
Author-email: contact@aibharata.com
License: Apache
Download-URL: https://github.com/aibharata/medicalAI/archive/v1.1.44.tar.gz
Description: <p align="center">
          <a href="https://aibharata.github.io/medicalAI/"><img src="https://raw.githubusercontent.com/aibharata/medicalAI/master/logo/logo.png" alt="MedicalAI"></a>
        </p>
        <p align="center">
            <em>Medical-AI is a AI framework specifically for Medical Applications</em>
        </p>
        
        
        ---
        
        **Documentation**: <a href="https://aibharata.github.io/medicalAI/" target="_blank">https://aibharata.github.io/medicalAI/</a>
        
        **Source Code**: <a href="https://github.com/aibharata/medicalai" target="_blank">https://github.com/aibharata/medicalai</a>
        
        ---
        
        [![Documentation Status](https://readthedocs.org/projects/medicalai/badge/?version=latest)](https://medicalai.readthedocs.io/en/latest/?badge=latest) [![Gitter](https://badges.gitter.im/aibh-medicalAI/devteam.svg)](https://gitter.im/aibh-medicalAI/devteam?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
        
        Medical-AI is a AI framework  for rapid prototyping of AI for Medical Applications.
        
        ## Installation
        
        <div class="termy">
        
        ```console
        $ pip install medicalai
        
        ---> 100%
        ```
        
        </div>
        ## 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
        ```py 
        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.
        ```py 
        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
        ```py 
        print(trainSet.data.shape)
        print(trainSet.labels.shape)
        ```
        
        ### Run Training and Save Model
        ```py
        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
        ```py
        trainer.plot_train_acc_loss()
        ```
        
        ### Plot Confusion matrix of test data
        ```py
        trainer.plot_confusion_matrix(labelNames,title='Confusion Matrix of Trained Model on Test Dataset')
        ```
        
        ### Loading Model for Prediction 
        ```py
        model = mai.load_model_and_weights(modelName = 'PATH_WHERE_MODEL_IS_SAVED_TO')
        ```
        
        
        ### Predict With Labels 
        ```py
        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
        ```py
        model.predict(testSet.data[0:2])
        ```
        
        ## Advanced Usage
        
        ### Code snippet for Training Using Medical-AI 
        ```py
        ## 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)
        ```
        
        ## Automated Tests
        To Check the tests
        
                pytest
        
        To See Output of Print Statements
        
                pytest -s 
        
Keywords: AI Framework,Medical AI,Tensorflow,radiology AI
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5, <3.8
Description-Content-Type: text/markdown
Provides-Extra: test
