Research on Skull Defect Recognition Based on Residual Neural Network

LIAN Hong-yu; SHEN Xiang; CHEN Guang-xin; GUO Wei; CAI Ying

doi:10.12263/newIT.2024.05.001

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Research on Skull Defect Recognition Based on Residual Neural Network

RESEARCH PAPER | 更新时间：2025-05-23

- Research on Skull Defect Recognition Based on Residual Neural Network
- New Generation of Information Technology Vol. 7, Issue 5, Pages: 1-5(2024)
- 作者机构：
  
  1.牡丹江医学院附属红旗医院，黑龙江牡丹江 157011
  2.牡丹江医学院第一临床医学院，黑龙江牡丹江 157011
  3.牡丹江医学院医学影像学院，黑龙江牡丹江 157011
  4.牡丹江医学院生命科学学院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2024.05.001
  CLC： T181
- Published：15 May 2024
- 稿件说明：
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廉洪宇, 沈翔, 陈广新, 等. 基于残差神经网络的颅骨缺损识别研究[J]. 新一代信息技术, 2024, 7(5): 01-05

LIAN Hong-yu, SHEN Xiang, CHEN Guang-xin, et al. Research on skull defect recognition based on residual neural network[J]. New Generation of Information Technology, 2024, 7(5): 01-05
廉洪宇, 沈翔, 陈广新, 等. 基于残差神经网络的颅骨缺损识别研究[J]. 新一代信息技术, 2024, 7(5): 01-05 DOI： 10.12263/newIT.2024.05.001.

LIAN Hong-yu, SHEN Xiang, CHEN Guang-xin, et al. Research on skull defect recognition based on residual neural network[J]. New Generation of Information Technology, 2024, 7(5): 01-05 DOI： 10.12263/newIT.2024.05.001.

摘要

鉴于颅骨缺损可能引发的严重健康问题，本研究旨在通过残差神经网络，提高对颅骨缺损的诊断精准度，从而提升患者的治疗成效和生存质量。本研究采用ResNet50深度学习模型结合颅骨医学影像数据，利用残差网络结构来增强对颅骨缺损的识别能力，并与传统CNN模型进行比较。ResNet50模型在颅骨缺损识别任务中显著优于CNN，其AUC指标达到0.92，表明在学习和泛化骨骼断裂特征方面具有更高的准确性和效率。综合考虑模型的计算资源需求、泛化性能以及实际应用中的数据规模，ResNet50模型在颅骨缺损识别领域展现出显著优势，但为了更好地适应临床应用，仍需对模型进行进一步优化和调整。

Abstract

In view of the serious health problems that may be caused by skull defects

this study aims to improve the accuracy of diagnosis of skull defects through residual neural networks

so as to improve the treatment effectiveness and quality of life of patients. In this study

ResNet50 deep learning model is combined with cranial medical image data

and residual network structure is used to enhance the recognition ability of skull defect

and the traditional CNN model is compared. ResNet50 model is significantly better than CNN in skull defect recognition task

and its AUC index reaches 0.92

indicating higher accuracy and efficiency in learning and generalizing bone fracture characteristics. Considering the computing resource requirements

generalization performance and data scale in practical applications

ResNet50 model shows significant advantages in skull defect recognition

but in order to better adapt to clinical applications

further optimization and adjustment of the model is still needed.

关键词

Keywords

references

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