{"title":"使用 ResNet50 卷积块注意力模块进行脑肿瘤分类","authors":"Oladosu Oyebisi Oladimeji, A. Ibitoye","doi":"10.1108/aci-09-2023-0022","DOIUrl":null,"url":null,"abstract":"PurposeDiagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.Design/methodology/approachTo selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.FindingsThe ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.Practical implicationsSince ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.Originality/valueThis research has not been published anywhere else.","PeriodicalId":37348,"journal":{"name":"Applied Computing and Informatics","volume":null,"pages":null},"PeriodicalIF":12.3000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Brain tumor classification using ResNet50-convolutional block attention module\",\"authors\":\"Oladosu Oyebisi Oladimeji, A. Ibitoye\",\"doi\":\"10.1108/aci-09-2023-0022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeDiagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.Design/methodology/approachTo selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.FindingsThe ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.Practical implicationsSince ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.Originality/valueThis research has not been published anywhere else.\",\"PeriodicalId\":37348,\"journal\":{\"name\":\"Applied Computing and Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.3000,\"publicationDate\":\"2023-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/aci-09-2023-0022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/aci-09-2023-0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Brain tumor classification using ResNet50-convolutional block attention module
PurposeDiagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.Design/methodology/approachTo selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.FindingsThe ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.Practical implicationsSince ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.Originality/valueThis research has not been published anywhere else.
期刊介绍:
Applied Computing and Informatics aims to be timely in disseminating leading-edge knowledge to researchers, practitioners and academics whose interest is in the latest developments in applied computing and information systems concepts, strategies, practices, tools and technologies. In particular, the journal encourages research studies that have significant contributions to make to the continuous development and improvement of IT practices in the Kingdom of Saudi Arabia and other countries. By doing so, the journal attempts to bridge the gap between the academic and industrial community, and therefore, welcomes theoretically grounded, methodologically sound research studies that address various IT-related problems and innovations of an applied nature. The journal will serve as a forum for practitioners, researchers, managers and IT policy makers to share their knowledge and experience in the design, development, implementation, management and evaluation of various IT applications. Contributions may deal with, but are not limited to: • Internet and E-Commerce Architecture, Infrastructure, Models, Deployment Strategies and Methodologies. • E-Business and E-Government Adoption. • Mobile Commerce and their Applications. • Applied Telecommunication Networks. • Software Engineering Approaches, Methodologies, Techniques, and Tools. • Applied Data Mining and Warehousing. • Information Strategic Planning and Recourse Management. • Applied Wireless Computing. • Enterprise Resource Planning Systems. • IT Education. • Societal, Cultural, and Ethical Issues of IT. • Policy, Legal and Global Issues of IT. • Enterprise Database Technology.