{"title":"基于深度CNN的迁移学习方法在脑肿瘤分类中的应用","authors":"Manish K. Arya, Rajeev Agrawal","doi":"10.46300/91011.2023.17.1","DOIUrl":null,"url":null,"abstract":"Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.","PeriodicalId":53488,"journal":{"name":"International Journal of Biology and Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Brain Tumor Classification Using Deep CNN-Based Transfer Learning Approach\",\"authors\":\"Manish K. Arya, Rajeev Agrawal\",\"doi\":\"10.46300/91011.2023.17.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.\",\"PeriodicalId\":53488,\"journal\":{\"name\":\"International Journal of Biology and Biomedical Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Biology and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46300/91011.2023.17.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biology and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46300/91011.2023.17.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
Brain Tumor Classification Using Deep CNN-Based Transfer Learning Approach
Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.
期刊介绍:
Topics: Molecular Dynamics, Biochemistry, Biophysics, Quantum Chemistry, Molecular Biology, Cell Biology, Immunology, Neurophysiology, Genetics, Population Dynamics, Dynamics of Diseases, Bioecology, Epidemiology, Social Dynamics, PhotoBiology, PhotoChemistry, Plant Biology, Microbiology, Immunology, Bioinformatics, Signal Transduction, Environmental Systems, Psychological and Cognitive Systems, Pattern Formation, Evolution, Game Theory and Adaptive Dynamics, Bioengineering, Biotechnolgies, Medical Imaging, Medical Signal Processing, Feedback Control in Biology and Chemistry, Fluid Mechanics and Applications in Biomedicine, Space Medicine and Biology, Nuclear Biology and Medicine.