基于ResNet50的脑胶质瘤甲基转移酶生物标志检测
Glioma Methyltransferase Biogenetic Markers Detection Based on ResNet50
DOI: 10.12677/ACM.2022.1291264, PDF,   
作者: 苏庆华, 张一晨*, 杨翼臣, 赫英男：北京物资学院信息学院，北京；刘 瑶：中科院计算机技术研究所，北京；杨学东*：中国中医科学院广安门医院，北京
关键词: 脑胶质瘤；甲基转移酶；图像处理；模型分类；ResNet50；Glioma； Methyltransferase； Image Processing； Model Classification； ResNet50
摘要: 生命科学和计算机科学技术的迅猛发展不仅带动了人们对肿瘤疾病机制的认识，而且随着人工智能、机器学习技术的成熟，在治疗方面也提高了脑部治疗的精准程度。脑癌的肿瘤标志物——甲基转移酶是判断脑部肿瘤良恶的检查标志。为提高重要生物遗传标志状态，解决生物遗传标志的预测问题，本文采用ResNet网络对脑癌治疗中重要生物遗传标志的状态检测，通过对公开的脑胶质瘤重要生物遗传标志数据集进行分析，对重要生物遗传标志特征进行分析，通过对数据模型进行训练，并在测试集进行实验验证，实验结果表明该方法能有效检测遗传标志。
Abstract: The rapid development of life science, computer science and technology not only promotes the un-derstanding of people’s tumor disease mechanism, but also improves the accuracy of brain treat-ment with the maturity of artificial intelligence and machine learning technology. Methyltransfer-ase—a tumor biogenetic marker of brain disease, is an examination marker to judge whether Glio-ma is good or bad. In order to improve the status of important biogenetic markers and solve the prediction problem of biogenetic markers, this paper uses ResNet network to segment the status of important biogenetic markers in brain cancer treatment, analyzes the data set of important bioge-netic markers of glioma, and analyzes the characteristics of important biogenetic markers. Experi-mental results show that this method can effectively segment genetic markers.
文章引用：苏庆华, 张一晨, 杨翼臣, 赫英男, 刘瑶, 杨学东. 基于ResNet50的脑胶质瘤甲基转移酶生物标志检测[J]. 临床医学进展, 2022, 12(9): 8756-8764. https://doi.org/10.12677/ACM.2022.1291264

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