Pre-processing and training a Ridge model for bone fracture detection

A side project for practice

What is a Ridge Model?

Ridge regression is a type of regularized linear regression method that adds a penalty term to the ordinary least squares objective function. This penalty term shrinks the coefficient estimates towards zero, which helps to reduce the variance in the estimates and prevent overfitting.

A ridge regression model uses L2 regularization, which adds a penalty term that is proportional to the square of the magnitude of the coefficients. The hyperparameter alpha controls the strength of the regularization, with higher values of alpha leading to stronger regularization and smaller coefficient estimates.

In practice, ridge regression is often used when there are many predictor variables and some of them are highly correlated.

The Code for the Project:

The code performs pre-processing on a set of bone fracture images and then trains a Ridge regression model to detect bone fractures in new images.

Step 1: Import required libraries

The code starts by importing the required libraries including PIL, cv2, numpy, sklearn.linear_model, sklearn.neighbors, sklearn.tree, sklearn.ensemble, and pickle.

Step 2: Define image size and image processing functions

The code defines WIDTH and HEIGHT constants for resizing the images, and then defines functions for resizing and reshaping the images.

The resize_and_save function takes an image name as input, opens the image using the PIL library, resizes it to the predefined size, and then saves the resized image.

The _reshape_img function takes a numpy array of an image as input, flattens the array, and returns a 1D array.

The _create_data function takes a list of image names and a list of corresponding labels as input, and then returns a numpy array of flattened image arrays and a numpy array of corresponding label arrays.

Step 3: Train a Ridge model

The code trains a Ridge regression model on the preprocessed images using the train_and_save function. If a saved model exists, it loads the model. Otherwise, it trains a new model using the Ridge regression algorithm and then saves it.

Step 4: Test the model

The code tests the trained Ridge regression model on the preprocessed test images using the score method, which returns the coefficient of determination (R^2) of the model. The R^2 score is printed for both the training and test sets.

Step 5: Conclusion

The code successfully preprocessed the bone fracture images and trained a Ridge regression model to detect bone fractures in new images.

You can download the code from my Github:
Code for the project