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  # Tuberculosis Classifier
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  # Model description
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  This is a computer vision model that was built with TensorFlow to classify if a given x-ray scan is positive for Tuberculosis or not.
 
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  #Intended uses & limitations
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  The model was built to help support low-resourced and short-staffed primary healthcare centers in Nigeria. Particularly, the aim to was created a computer-aided diagnosing tool for Radiologists in these centers.
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  The model has not undergone clinical testing and usage is at ueser's own risk.The model has however been tested on real life data images that are positive for tuberculosis
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- How to use
 
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  Download the pre-trained model and use it to make inference.
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  A space has been created for testing (here)[space.com]
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- Training data
 
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  The entire dataset consist of 3500 negative images and 700 positive TB images. </br>
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  The data was splitted in 80% for training and 20% for validation.
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- Training procedure
 
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  Evaluation results
 
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  # Tuberculosis Classifier
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  # Model description
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  This is a computer vision model that was built with TensorFlow to classify if a given x-ray scan is positive for Tuberculosis or not.
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+
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  #Intended uses & limitations
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  The model was built to help support low-resourced and short-staffed primary healthcare centers in Nigeria. Particularly, the aim to was created a computer-aided diagnosing tool for Radiologists in these centers.
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  The model has not undergone clinical testing and usage is at ueser's own risk.The model has however been tested on real life data images that are positive for tuberculosis
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+
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+ #How to use
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  Download the pre-trained model and use it to make inference.
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  A space has been created for testing (here)[space.com]
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+
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+ #Training data
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  The entire dataset consist of 3500 negative images and 700 positive TB images. </br>
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  The data was splitted in 80% for training and 20% for validation.
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+ #Training procedure
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+ Transfer-learning was employed using InceptionV3 as the pre-trained model. The classes were weighted during training in order to neutralize the imbalanced class.
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  Evaluation results