Classify Broken and Normal Bones in X-ray Images using deep learning

What is classification?

A Classification model is a program that learns how to assign a label (or class) to input data. Classification belongs to the category of supervised learning where the model is trained with images and their classes as input to predict labels for new images. In this project, we use binary classification which refers to classification tasks with two class labels. The ‘0’ label is assigned to the broken bone class and ‘1’ represents the normal bone class. Classification algorithms are evaluated based on their results. There are different evaluation metrics but we will use 3 of them for this project.

Accuracy

Accuracy is a popular and simple metric used to evaluate the performance of a model based on the predicted labels.

Precision

Precision is the ratio of True Positives to all the positives predicted by the model. The more false positives the model predicts, the lower its precision will be.

Recall

Recall is the ratio of true positives to all the positives in your dataset. The more false negatives the model predicts, the lower its recall will be.