Deep feature extraction based brain image classification model using preprocessed images: PDRNet

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dc.authoridTaşcı, Burak/0000-0002-4490-0946
dc.authorwosidTaşcı, Burak/L-7100-2018
dc.contributor.authorTasci, Burak
dc.contributor.authorTasci, Irem
dc.date.accessioned2024-08-04T20:57:35Z
dc.date.available2024-08-04T20:57:35Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: Stroke is a neurological condition that occurs when cerebral vessels become blocked and have reduced blood flow. This research proposes a hybrid deep feature-based feature engineering model to achieve high classification performance. Materials and method: In this research, three brain magnetic resonance image datasets were used to test the proposed model. A deep feature engineering model has been proposed to deploy the raw MRI and four pre-processing algorithms: GradCAM, histogram-matching, canny edge detection, and Locally Interpretable Model-Agnostic Explanations(LIME). The deep features have been extracted using Resnet101 and DenseNet201 pre-trained convolutional neural networks (CNN). Thus, this model is titled preprocessing based DenseNet and ResNet (PDRNet). The iterative neighborhood component analysis (INCA) function selects the most suitable features. These features are trained and validated using support vector machine (SVM) classifiers. Iterative Majority Voting (IMV) has been applied to the results obtained from the SVM. The best classification result has been selected by deploying IMV. Results: Our proposed PDRNet achieved a classification accuracy of 97.56% for Dataset 1, 99.32% for Dataset 2, and 99.16% for Dataset 3. The success of the presented model is demonstrated using these calculated accuracies. Conclusions: Our proposed hybrid deep feature model was tested on two datasets with two and four classes. It has also been compared to other state-of-art deep learning-based models, and our model performs better. These results and findings clearly demonstrate the success of the introduced hybrid deep feature engineering method.en_US
dc.identifier.doi10.1016/j.bspc.2022.103948
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103948
dc.identifier.urihttps://hdl.handle.net/11616/102752
dc.identifier.volume78en_US
dc.identifier.wosWOS:000883035800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
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.subjectHybrid deep feature engineeringen_US
dc.subjectTransfer learningen_US
dc.subjectIschemic acute infarction detectionen_US
dc.subjectDiffusion MRIen_US
dc.titleDeep feature extraction based brain image classification model using preprocessed images: PDRNeten_US
dc.typeArticleen_US

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