{"title":"使用 CNN-LSTM 方法和基于图像的 GLCM 特征对颅内出血(CT)图像进行分类","authors":"Swetha Mucha, A. Ramesh Babu","doi":"10.1051/matecconf/202439201075","DOIUrl":null,"url":null,"abstract":"A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.","PeriodicalId":18309,"journal":{"name":"MATEC Web of Conferences","volume":" 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of intracranial hemorrhage (CT) images using CNN-LSTM method and image-based GLCM features\",\"authors\":\"Swetha Mucha, A. Ramesh Babu\",\"doi\":\"10.1051/matecconf/202439201075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.\",\"PeriodicalId\":18309,\"journal\":{\"name\":\"MATEC Web of Conferences\",\"volume\":\" 11\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MATEC Web of Conferences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/matecconf/202439201075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MATEC Web of Conferences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/matecconf/202439201075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of intracranial hemorrhage (CT) images using CNN-LSTM method and image-based GLCM features
A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.
期刊介绍:
MATEC Web of Conferences is an Open Access publication series dedicated to archiving conference proceedings dealing with all fundamental and applied research aspects related to Materials science, Engineering and Chemistry. All engineering disciplines are covered by the aims and scope of the journal: civil, naval, mechanical, chemical, and electrical engineering as well as nanotechnology and metrology. The journal concerns also all materials in regard to their physical-chemical characterization, implementation, resistance in their environment… Other subdisciples of chemistry, such as analytical chemistry, petrochemistry, organic chemistry…, and even pharmacology, are also welcome. MATEC Web of Conferences offers a wide range of services from the organization of the submission of conference proceedings to the worldwide dissemination of the conference papers. It provides an efficient archiving solution, ensuring maximum exposure and wide indexing of scientific conference proceedings. Proceedings are published under the scientific responsibility of the conference editors.