论文标题
使用CT扫描图像通过CNN与懒惰学习方法的颅骨骨折分类
Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach
论文作者
论文摘要
对于放射科医生和研究人员来说，头骨骨折的分类是一项具有挑战性的任务。颅骨骨折会导致骨骼骨折，可切入大脑并引起出血和其他损伤类型。因此，很早地检测和对骨折进行分类至关重要。在现实世界中，通常在多个部位发生骨折。这使得很难检测到许多断裂类型可能总结颅骨骨折的断裂类型。不幸的是，手动检测头骨骨折和分类过程是耗时的，威胁了患者的生命。由于深度学习的出现，这一过程可以自动化。卷积神经网络（CNN）是最广泛使用的图像分类的深度学习模型，因为与其他模型相比，它们具有很高的精度和出色的结果。我们提出了一个名为SkullNETV1的新模型，该模型通过利用CNN进行特征提取和懒惰学习方法，该模型包括新型CNN，该方法充当了从脑CT图像分类的颅骨骨折分类的分类器来对五种断裂类型进行分类。我们建议的模型的子集准确度为88％，F1得分为93％，曲线（AUC）下的面积为0.89至0.98，对这个七级多标签分类的锤量分数为92％，锤损损失为0.04。
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.
