# Predicting skull fractures via CNN with classification algorithms

arXiv: https://arxiv.org/abs/2208.06756 · HTML: https://mlsanity.com/paper/2208.06756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach](https://mlsanity.com/paper/2203.10786/) (2022)

## Built on by (most cited first, 1 of 1)

- [CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19](https://mlsanity.com/paper/2302.09004/) (2023)

## Abstract (arXiv)

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.