An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

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dc.authoridTaşcı, Burak/0000-0002-4490-0946
dc.authoridTASCI, IREM/0000-0001-7069-769X
dc.authoridDOGAN, Sengul/0000-0001-9677-5684
dc.authoridAcharya, Rajendra U/0000-0003-2689-8552
dc.authoridBarua, Prabal Datta/0000-0001-5117-8333
dc.authoridTan, Ru San/0000-0003-2086-6517
dc.authoridFaust, Oliver/0000-0002-3979-4077
dc.authorwosidTUNCER, Turker/W-4846-2018
dc.authorwosidTaşcı, Burak/L-7100-2018
dc.authorwosidTASCI, IREM/AAW-3048-2020
dc.authorwosidDOGAN, Sengul/W-4854-2018
dc.authorwosidTan, Ru San/HJI-5085-2023
dc.authorwosidAcharya, Rajendra U/E-3791-2010
dc.contributor.authorMacin, Gulay
dc.contributor.authorTasci, Burak
dc.contributor.authorTasci, Irem
dc.contributor.authorFaust, Oliver
dc.contributor.authorBarua, Prabal Datta
dc.contributor.authorDogan, Sengul
dc.contributor.authorTuncer, Turker
dc.date.accessioned2024-08-04T20:56:27Z
dc.date.available2024-08-04T20:56:27Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMultiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 x 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.en_US
dc.identifier.doi10.3390/app12104920
dc.identifier.issn2076-3417
dc.identifier.issue10en_US
dc.identifier.urihttps://doi.org/10.3390/app12104920
dc.identifier.urihttps://hdl.handle.net/11616/102304
dc.identifier.volume12en_US
dc.identifier.wosWOS:000801393200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmultiple sclerosisen_US
dc.subjectmagnetic resonance imagingen_US
dc.subjectfeature engineeringen_US
dc.subjectlocal phase quantizationen_US
dc.titleAn Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQen_US
dc.typeArticleen_US

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