{"title":"使用复杂网络的MRI脑肿瘤特征提取的高性能方法。","authors":"Thanh Han Trong, Hinh Nguyen Van, Luu Vu Dang","doi":"10.1155/2023/8843488","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To localize and distinguish between benign and malignant tumors on MRI.</p><p><strong>Method: </strong>This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. <i>Experiments and Results</i>. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%.</p><p><strong>Conclusion: </strong>The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.</p>","PeriodicalId":8029,"journal":{"name":"Applied Bionics and Biomechanics","volume":"2023 ","pages":"8843488"},"PeriodicalIF":1.8000,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539089/pdf/","citationCount":"0","resultStr":"{\"title\":\"High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network.\",\"authors\":\"Thanh Han Trong, Hinh Nguyen Van, Luu Vu Dang\",\"doi\":\"10.1155/2023/8843488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To localize and distinguish between benign and malignant tumors on MRI.</p><p><strong>Method: </strong>This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. <i>Experiments and Results</i>. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%.</p><p><strong>Conclusion: </strong>The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.</p>\",\"PeriodicalId\":8029,\"journal\":{\"name\":\"Applied Bionics and Biomechanics\",\"volume\":\"2023 \",\"pages\":\"8843488\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539089/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Bionics and Biomechanics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/8843488\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Bionics and Biomechanics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/8843488","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network.
Objective: To localize and distinguish between benign and malignant tumors on MRI.
Method: This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. Experiments and Results. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%.
Conclusion: The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.
期刊介绍：
Applied Bionics and Biomechanics publishes papers that seek to understand the mechanics of biological systems, or that use the functions of living organisms as inspiration for the design new devices. Such systems may be used as artificial replacements, or aids, for their original biological purpose, or be used in a different setting altogether.
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