Predicting skull fractures via CNN with classification algorithms
This work addresses the need for a computer-assisted system to help physicians diagnose skull fractures more accurately and quickly from CT images, though it is incremental as it applies existing methods to a specific medical domain.
The paper tackled skull fracture classification from brain CT scans using a CNN-based approach, achieving an F1-score of 96% and other high metrics by combining ResNet50 for feature extraction with a gradient boosted decision tree classifier.
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.