Here we attempt to create an algorithm to classify images for bone fracture, for this an input image is given and output is given as a “Positive” or “Negative” label. The input data is in the form of X Ray images of the bones. Hence, an appropriate supervised learning model is to be trained with the data to give correct label to the input image to predict a fracture. Some preprocessing of the data (converting RGB images to Grayscale) is also necessary here. This repository contains a Keras implementation of a 169 layer Densenet Model on MURA dataset
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Here, I trained the Densenet on XR_HUMERUS of the dataset for 52 epochs with a batch size of 8.
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To load the dataset you can use the function data_loader.py.
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To train the model run the file mura.py.
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To load the model run the file model_test.py.
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To get these particular graphs run the following files:
- Training Accuracy: plot_results_train_acc.py
- Validation Accuracy: plot_results_valid_acc.py
- Training loss: plot_results_train_loss.py
- Validation Loss: plot_results_valid_loss.py
