Brain tumor detection with multi-scale fractal feature network and fractal residual learning

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dc.authorid0000-0002-7201-6963
dc.authorid0000-0003-1840-9958
dc.contributor.authorJakhar, Shyo Prakash
dc.contributor.authorNandal, Amita
dc.contributor.authorDhaka, Arvind
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T19:58:59Z
dc.date.available2024-09-25T19:58:59Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description.abstractDeep learning has enabled the creation of several approaches for segmenting brain tumors using convolutional neural networks. These methods have come about as a direct result of the advancement of the field of machine learning. The proposed pixel-level segmentation is based on fractal residual deep learning; provide an insufficient degree of sensitivity when used for tumor segmentation. This is achieved due to fractal feature extraction and multi-scale approach used for segmentation. If multi-level segmentation is used, it is possible to effectively increase the sensitivity of the segmentation process which is the additional benefit from the proposed method. In this work, the production of tumor region is based on multi-scale pixel segmentation. This approach protects the integrity of tumor information while simultaneously improving the detection accuracy by cutting down on the total number of tumor regions. When compared to the information about the brain found in tumor locations, the proposed strategy has the potential to enhance the percentage of brain tumor information. This work proposes a novel network structure known as the Mutli-scale fractal feature network (MFFN) to increase the accuracy of the network's classification as well as its sensitivity when it comes to the segmentation of brain tumors. The proposed method with overall feature results in 94.66% accuracy, 94.42% sensitivity and 92.81% specificity using 5fold cross validation. In this paper the Cancer Imaging Archive (TCIA) dataset has been used in order to evaluate performance evaluation metrics and segmentation results to quantify the superiority of proposed brain tumor detection approach in comparison to existing methods.en_US
dc.identifier.doi10.1016/j.asoc.2024.111284
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85185157092en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2024.111284
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13842
dc.identifier.volume153en_US
dc.identifier.wosWOS:001172102200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectMutli-scale Fractal Feature Networken_US
dc.subjectFractal Residual Networken_US
dc.subjectMulti-scale Pixel Segmentationen_US
dc.subjectClassificationen_US
dc.subjectSegmentationen_US
dc.subjectPixel-level Segmentationen_US
dc.titleBrain tumor detection with multi-scale fractal feature network and fractal residual learningen_US
dc.typeArticleen_US

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