HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection

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dc.authorid0000-0002-3929-4523en_US
dc.authorid0000-0003-4782-0420en_US
dc.authorid0000-0003-1150-2690en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorSharma, Arpit Kumar
dc.contributor.authorNandal, Amita
dc.contributor.authorDhaka, Arvind
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlwadie, Raghad
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2023-09-04T10:15:43Z
dc.date.available2023-09-04T10:15:43Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThe authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 223202.en_US
dc.description.abstractBrain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if not cured at an early stage. In spite of several promising results and substantial efforts in this research area, the real challenge is to provide the accurate classification and segmentation. The key issue in brain tumor detection develops from the irregular changes in the tumor size, shape and location. In assessing the MRI images, computer-aided diagnoses are playing an extraordinary role and can help clinicians/radiologist. Nowadays, brain tumor has become the most incursive ailment that leads to a very short life expectancy when it reaches its highest grade. This research paper has created a new model using histogram of gradient (HOG) based neural features from MRI images for tumors detection. This research has conducted the feature optimization approach to achieve additional instinctive features from the complex feature vector. We developed a Modified ResNet50 model with HOG technique. The modified ResNet50 model can accurately extract the deep feature using deep learning approach. This model is applied along with the upgraded layered architecture in order to keep the optimal computational efficiency. We have also used the augmentation and feature extraction techniques using machine learning-based ensemble classifier that further provides the optimized fusion vector to identify the tumor. Such hybrid approach provides excellent performance with the detection accuracy of 88% with HOG and modified ResNet50. The results are also compared with the recent state of art methods.en_US
dc.description.sponsorshipDeputyship for Research & Innovation, Ministry of Education in Saudi Arabia [223202]en_US
dc.identifier.citationSharma, A. K., Nandal, A., Dhaka, A., Polat, K., Alwadie, R., Alenezi, F., & Alhudhaif, A. (2023). HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection. Biomedical Signal Processing and Control, 84, 104737.en_US
dc.identifier.doi10.1016/j.bspc.2023.104737
dc.identifier.endpage12en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85149176034en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.bspc.2023.104737
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11648
dc.identifier.volume84en_US
dc.identifier.wosWOS:000949986900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Science 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.subjectBrain Tumor Diagnosisen_US
dc.subjectModified ResNet50en_US
dc.subjectHOG Techniqueen_US
dc.subjectConvolutional Neural-Networksen_US
dc.subjectSegmentationen_US
dc.subjectFusionen_US
dc.titleHOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detectionen_US
dc.typeArticleen_US

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