Classification of skull fracture is a challenging task for both radiologists
and researchers. Skull fractures result in broken pieces of bone, which can cut
into the brain and cause bleeding and other injury types. So it is vital to
detect and classify the fracture very early. In real world, often fractures
occur at multiple sites. This makes it harder to detect the fracture type where
many fracture types might summarize a skull fracture. Unfortunately, manual
detection of skull fracture and the classification process is time-consuming,
threatening a patient's life. Because of the emergence of deep learning, this
process could be automated. Convolutional Neural Networks (CNNs) are the most
widely used deep learning models for image categorization because they deliver
high accuracy and outstanding outcomes compared to other models. We propose a
new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN
for feature extraction and lazy learning approach which acts as a classifier
for classification of skull fractures from brain CT images to classify five
fracture types. Our suggested model achieved a subset accuracy of 88%, an F1
score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score
of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled
classification.

提出了一种新的模型 SkullNetV1，利用卷积神经网络和懒惰学习方法对脑 CT 图像中的颅骨骨折进行分类，可以自动化地检测和分类骨折类型，该模型在七类多标签分类中取得了 88％的子集准确度，93％的 F1 得分，0.89 至 0.98 的曲线下面积（AUC），92％的汉明分数和 0.04 的汉明损失。