Research on the Practical Classification and Privacy Protection of CT Images of Parotid Tumors based on ResNet50 Model

腮腺 计算机科学 集合(抽象数据类型) 深度学习 医学诊断 面神经 人工智能 模式识别(心理学) 医学 放射科 病理 程序设计语言
作者
Jiantin Yuan,Yangyang Fan,Xiaoyi Lv,Chen Chen,Debao Li,Hong Yu,Yan Wang
出处
期刊:Journal of physics [IOP Publishing]
卷期号:1576 (1): 012040-012040 被引量:9
标识
DOI:10.1088/1742-6596/1576/1/012040
摘要

Abstract Parotid gland disease is one of the main causes of facial paralysis, and parotid gland tumor is a great threat to the life of patients. The main diagnostic way of parotid diseases is imaging examination, so it is of great significance for the rapid classification of parotid image. In conclusion, 51 CT images of parotid malignant tumors and 101 CT images of parotid pleomorphic adenomas are selected as the research data set, and an intelligent and efficient machine learning algorithm is proposed for the practical classification of parotid images. At the same time, this paper also explores the privacy protection of medical images. Based on the advantages of deep learning, such as no feature engineering, strong adaptability and easy conversion, ResNet50 model in deep learning is selected as the basic network framework to achieve the purpose of rapid classification of parotid CT images. This is the first time that ResNet50 classification algorithm is applied to the practical classification of parotid tumor CT images. The results show that the accuracy of the test set converges to 90% when the model is iterated 1000 times, which also proves that this study has certain practical significance and application value for the auxiliary diagnosis of parotid gland tumor and other head and neck tumors. Simultaneously, this paper also explores the application of desensitization strategy in CT images of parotid tumors, which improves the performance of the model and also greatly protects the privacy of patients, and has a good application prospect in medical big data.
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