Predicting cardiac rhythm management devices (CRMDs) manufacturers using deep learning
Data processing:
- number_of_models - plots bar chart of number of each class in test and train data.
- mean_image - takes the mean of images within given folder.
- std_image - takes the SD of images within given folder.
- data_preprocessing - applies histogram equalisation to images.
- image_resizing - resizes images in given path to 227x227
- image_to_numpy - creates numpy arrays of data with size 150x150 or 227x227.
Model building:
- first model - builds and trains simple model.
- first_model_binary - builds and trains binary classification model.
- export_features - runs images through convolutional base and saves output
- load_features - takes output from export features and trains classifier
- InceptionV3_scratch - trains model with InceptionV3 architecture - not initialise on ImageNet.
- scratch_hyperas - building a model from scratch using hyper wrapper for grid search.
- conv_base_frozen - transfer learning using ImageNet weights.
- fine_tuning_final_run - similar to conv_base frozen, but fine tunes top layers.
- fold_creator - splits data into 10 folds for cross validation
- fold_directory - trains model with fine-tuning and cross-validation.
Data analysis:
- plot_tensorboard - takes tensor board training traces and plots using plotly.
- predicting_confusion - plots confusion matrices of predictions on test set.
Final programme:
- CRMD_predictor - makes a prediction of CRMD model on images stored in images_to_run folder.