Brain tumor classification using the modified ResNet50 model based on transfer learning

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dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorSharma, Arpit Kumar
dc.contributor.authorNandal, Amita
dc.contributor.authorDhaka, Arvind
dc.contributor.authorZhou, Liang
dc.contributor.authorAlenezi, Fayadh
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-05-22T10:43:53Z
dc.date.available2024-05-22T10:43:53Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work is supported by the Foundation of National Key R & D Program of China [Grant number 2020YFC2008700] ; the National Natural Science Foundation of China [Grant number 82072228] ; the Foundation of Shanghai Municipal Commission of Economy and Informatization [Grant number 202001007] ; the Foundation of the Program of Shanghai Academic/Technology Research Leader under the Science and Technology Innovation Action Plan [Grant number 22XD1401300] ; the Foundation of the Technical Standards Program Under the Science and Technology Innovation Action Plan [Grant number 23DZ2204100] .en_US
dc.description.abstractBrain tumour classification is essential for determining the type and grade and deciding on therapy appropriately. Several diagnostic methods are used in the therapeutic therapy to identify brain tumours. MRI, on the other hand, offers superior picture clarity, which is why specialists depend on it. Furthermore, detecting cancer through the manual division of brain tumours is a time-consuming, exhausting, and difficult job. The handdesigned outlines for planned brain tumour growth methods are present in the majority of the instances. Segmentation is a highly reliable and precise method for assessing therapy prognosis, planning, and outcomes. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) advancements have enabled us to investigate the illness with high precision in a short period of time. Such technologies have produced some remarkable results, particularly in the last twenty years. Such breakthroughs provide doctors with the ability to evaluate the human anatomy using high-resolution sections. The most recent approaches can improve diagnostic precision when examining patients using non-invasive means. This work introduces a brain tumour detection method. The model grows using ResNet50, feature extraction, and augmentation. CNN's pre-trained datasets are used to fine-tune transfer learning. The proposed design utilised elements of the ResNet50 model, removing the final layer and adding four additional layers to meet work conditions. This study uses the improved ResNet50 model to present a novel deep-learning approach based on a transfer learning technique for evaluating brain cancer categorisation accuracy. Performance metrics were used to evaluate the effectiveness of the proposed model, and the results were compared to those obtained using state-of-the-art methods.en_US
dc.description.sponsorshipFoundation of National Key R amp; D Program of China [2020YFC2008700]; National Natural Science Foundation of China [82072228]; Foundation of Shanghai Municipal Commission of Economy and Informatization [202001007]; Foundation of the Program of Shanghai Academic/Technology Research Leader under the Science and Technology Innovation Action Plan [22XD1401300]; Foundation of the Technical Standards Program Under the Science and Technology Innovation Action Plan [23DZ2204100]en_US
dc.identifier.citationSharma, A. K., Nandal, A., Dhaka, A., Zhou, L., Alhudhaif, A., Alenezi, F., & Polat, K. (2023). Brain tumor classification using the modified ResNet50 model based on transfer learning. Biomedical Signal Processing and Control, 86, 105299.en_US
dc.identifier.doi10.1016/j.bspc.2023.105299
dc.identifier.endpage14en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85166618367en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.bspc.2023.105299
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12145
dc.identifier.volume86en_US
dc.identifier.wosWOS:001050346600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Sci LTDen_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTransfer Learningen_US
dc.subjectDeep Learningen_US
dc.subjectFeature Extractionen_US
dc.subjectMachine Learningen_US
dc.subjectMRI and Neuroimagingen_US
dc.subjectMRIen_US
dc.titleBrain tumor classification using the modified ResNet50 model based on transfer learningen_US
dc.typeArticleen_US

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