论文标题
通过CNN通过分类算法预测头骨骨折
Predicting skull fractures via CNN with classification algorithms
论文作者
论文摘要
计算机断层扫描（CT）图像对于诊断疾病已经非常重要。 CT扫描切片包含大量数据，可以使用正常的视觉检查使用必要的精度和速度来正确检查这些数据。需要计算机辅助的头骨骨折分类专家系统来协助医生。卷积神经网络（CNN）是图像分类的最广泛的深度学习模型，因为在准确性和结果方面，它们通常超过其他模型。然后开发和测试CNN模型，并比较了几个卷积神经网络（CNN）结构。 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.
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.
