Predicting skull fractures via CNN with classification algorithms

TL;DR

This paper develops a CNN-based system combined with machine learning algorithms to accurately classify skull fractures from CT images, aiding medical diagnosis.

Contribution

It introduces a hybrid approach using ResNet50 for feature extraction and gradient boosting for classification, achieving high accuracy in skull fracture detection.

Findings

ResNet50 combined with gradient boosting achieved 96% F1-score.

The system outperformed other CNN architectures in accuracy.

High ROC AUC indicates reliable classification performance.

Abstract

Computer Tomography (CT) images have become quite important to diagnose diseases. CT scan slice contains a vast amount of data that may not be properly examined with the requisite precision and speed using normal visual inspection. A computer-assisted skull fracture classification expert system is needed to assist physicians. Convolutional Neural Networks (CNNs) are the most extensively used deep learning models for image categorization since most often time they outperform other models in terms of accuracy and results. The CNN models were then developed and tested, and several convolutional neural network (CNN) architectures were compared. ResNet50, which was used for feature extraction combined with a gradient boosted decision tree machine learning algorithm to act as a classifier for the categorization of skull fractures from brain CT scans into three fracture categories, had the best overall F1-score of 96%, Hamming Score of 95%, Balanced accuracy Score of 94% & ROC AUC curve of 96% for the classification of skull fractures.