Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm
dc.authorid | Tirkolaee, Erfan Babaee/0000-0003-1664-9210 | |
dc.authorid | Ranjbarzadeh, Ramin/0000-0001-7065-9060 | |
dc.authorwosid | Tirkolaee, Erfan Babaee/U-3676-2017 | |
dc.authorwosid | Ranjbarzadeh, Ramin/E-7289-2019 | |
dc.contributor.author | Ranjbarzadeh, Ramin | |
dc.contributor.author | Zarbakhsh, Payam | |
dc.contributor.author | Caputo, Annalina | |
dc.contributor.author | Tirkolaee, Erfan Babaee | |
dc.contributor.author | Bendechache, Malika | |
dc.date.accessioned | 2024-05-19T14:39:34Z | |
dc.date.available | 2024-05-19T14:39:34Z | |
dc.date.issued | 2024 | |
dc.department | İstinye Üniversitesi | en_US |
dc.description.abstract | Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks. | en_US |
dc.description.sponsorship | Science Foundation Ireland [18/CRT/6183, 13/RC/2106_P2, 13/RC/2094_P2] | en_US |
dc.description.sponsorship | This research was supported by Science Foundation Ireland under Grant numbers 18/CRT/6183 (SFI Center for Research Training in Machine Learning) , 13/RC/2106_P2 (ADAPT Center for Digital Content Technology) , and 13/RC/2094_P2 (Lero SFI Center for Software) . For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. | en_US |
dc.identifier.doi | 10.1016/j.compbiomed.2023.107723 | |
dc.identifier.issn | 0010-4825 | |
dc.identifier.issn | 1879-0534 | |
dc.identifier.pmid | 38000242 | en_US |
dc.identifier.scopus | 2-s2.0-85177867989 | en_US |
dc.identifier.scopusquality | Q1 | en_US |
dc.identifier.uri | https://doi.org10.1016/j.compbiomed.2023.107723 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12713/4805 | |
dc.identifier.volume | 168 | en_US |
dc.identifier.wos | WOS:001125006500001 | en_US |
dc.identifier.wosquality | N/A | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
dc.relation.ispartof | Computers In Biology and Medicine | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.snmz | 20240519_ka | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Improved Chimp Optimization Algorithm | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Feature Selection | en_US |
dc.title | Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm | en_US |
dc.type | Article | en_US |