Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/57794
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kaplan, Ela | en |
dc.contributor.author | Chan, Wai Yee | en |
dc.contributor.author | Altinsoy, Hasan Baki | en |
dc.contributor.author | Baygin, Mehmet | en |
dc.contributor.author | Barua, Prabal Datta | en |
dc.contributor.author | Chakraborty, Subrata | en |
dc.contributor.author | Dogan, Sengul | en |
dc.contributor.author | Tuncer, Turker | en |
dc.contributor.author | Acharya, U Rajendra | en |
dc.date.accessioned | 2024-03-15T05:03:42Z | - |
dc.date.available | 2024-03-15T05:03:42Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.citation | Journal of Digital Imaging, v.36, p. 2441-2460 | en |
dc.identifier.issn | 1618-727X | en |
dc.identifier.issn | 0897-1889 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/57794 | - |
dc.description.abstract | <p>Detecting 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.</p> | en |
dc.language | en | en |
dc.publisher | Springer New York LLC | en |
dc.relation.ispartof | Journal of Digital Imaging | en |
dc.title | PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1007/S10278-023-00889-8 | en |
local.contributor.firstname | Ela | en |
local.contributor.firstname | Wai Yee | en |
local.contributor.firstname | Hasan Baki | en |
local.contributor.firstname | Mehmet | en |
local.contributor.firstname | Prabal Datta | en |
local.contributor.firstname | Subrata | en |
local.contributor.firstname | Sengul | en |
local.contributor.firstname | Turker | en |
local.contributor.firstname | U Rajendra | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | schakra3@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | United States of America | en |
local.format.startpage | 2441 | en |
local.format.endpage | 2460 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 36 | en |
local.title.subtitle | Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI | en |
local.contributor.lastname | Kaplan | en |
local.contributor.lastname | Chan | en |
local.contributor.lastname | Altinsoy | en |
local.contributor.lastname | Baygin | en |
local.contributor.lastname | Barua | en |
local.contributor.lastname | Chakraborty | en |
local.contributor.lastname | Dogan | en |
local.contributor.lastname | Tuncer | en |
local.contributor.lastname | Acharya | en |
dc.identifier.staff | une-id:schakra3 | en |
local.profile.orcid | 0000-0002-0102-5424 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/57794 | en |
local.date.onlineversion | 2023-08-03 | - |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | PFP-HOG | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Kaplan, Ela | en |
local.search.author | Chan, Wai Yee | en |
local.search.author | Altinsoy, Hasan Baki | en |
local.search.author | Baygin, Mehmet | en |
local.search.author | Barua, Prabal Datta | en |
local.search.author | Chakraborty, Subrata | en |
local.search.author | Dogan, Sengul | en |
local.search.author | Tuncer, Turker | en |
local.search.author | Acharya, U Rajendra | en |
local.open.fileurl | https://rune.une.edu.au/web/retrieve/7d550cc6-5745-4933-8707-8d53f0ecf455 | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.available | 2023 | en |
local.year.published | 2023 | en |
local.fileurl.open | https://rune.une.edu.au/web/retrieve/7d550cc6-5745-4933-8707-8d53f0ecf455 | en |
local.fileurl.closedpublished | https://rune.une.edu.au/web/retrieve/7d550cc6-5745-4933-8707-8d53f0ecf455 | en |
local.subject.for2020 | 4601 Applied computing | en |
local.subject.seo2020 | TBD | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.date.moved | 2024-04-16 | en |
Appears in Collections: | Journal Article School of Science and Technology |
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