Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning

Abstract

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.