PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

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dc.authoridBaygin, Mehmet/0000-0001-6449-8950en_US
dc.authoridAcharya, Rajendra U/0000-0003-2689-8552en_US
dc.authoridDOGAN, Sengul/0000-0001-9677-5684en_US
dc.authoridChakraborty, Subrata/0000-0002-0102-5424en_US
dc.authorscopusid55669814900en_US
dc.authorscopusid57200310836en_US
dc.authorscopusid23983952500en_US
dc.authorscopusid55293658600en_US
dc.authorscopusid36993665100en_US
dc.authorscopusid56377149900en_US
dc.authorscopusid25653093400en_US
dc.authorwosidBaygin, Mehmet/AAT-5720-2021en_US
dc.authorwosidAcharya, Rajendra U/E-3791-2010en_US
dc.authorwosidChan, Wai-Yee/ABE-3738-2020en_US
dc.authorwosidDOGAN, Sengul/W-4854-2018en_US
dc.authorwosidChakraborty, Subrata/IXX-0792-2023en_US
dc.contributor.authorKaplan, Ela
dc.contributor.authorChan, Wai Yee
dc.contributor.authorAltinsoy, Hasan Baki
dc.contributor.authorBaygin, Mehmet
dc.contributor.authorBarua, Prabal Datta
dc.contributor.authorChakraborty, Subrata
dc.contributor.authorDogan, Sengul
dc.date.accessioned2024-08-23T16:07:12Z
dc.date.available2024-08-23T16:07:12Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDetecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.en_US
dc.identifier.doi10.1007/s10278-023-00889-8
dc.identifier.endpage2460en_US
dc.identifier.issn0897-1889
dc.identifier.issn1618-727X
dc.identifier.issue6en_US
dc.identifier.pmid37537514en_US
dc.identifier.scopus2-s2.0-85166535800en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2441en_US
dc.identifier.urihttps://doi.org/10.1007/s10278-023-00889-8
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14538
dc.identifier.volume36en_US
dc.identifier.wosWOS:001041974600002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Digital Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain MRIen_US
dc.subjectPyramid and fixed-size patch feature extractionen_US
dc.subjectHOGen_US
dc.subjectBiomedical image processingen_US
dc.subjectComputer visionen_US
dc.subjectDiagnosisen_US
dc.subjectImagesen_US
dc.subjectModelen_US
dc.titlePFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRIen_US
dc.typeArticleen_US

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