Automated Classification of Brain Tumors by Deep Learning-Based Models on Magnetic Resonance Images Using a Developed Web-Based Interface

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dc.authoridtetik, bora/0000-0001-7696-7785
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridYASAR, Seyma/0000-0003-1300-3393
dc.authorwosidtetik, bora/AAA-8841-2021
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidUcuzal, Hasan/GWC-0566-2022
dc.authorwosidYaşar, Şeyma/ABI-8055-2020
dc.contributor.authorTetik, Bora
dc.contributor.authorUcuzal, Hasan
dc.contributor.authorYasar, Seyma
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:10:33Z
dc.date.available2024-08-04T20:10:33Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide techicalsupport to radiologists and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions. Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, To construct the proposed system which web-based interface and the deep learning-based models, the Keras/Auto-Keras library, which is employed in Python's programming language, is used. Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metrics were used for performance evaluations. Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465 instances) was employed in the testing phase. All the performance metrics were higher than 98% for the classification of brain tumors on the training data set. Similarly, all the evaluation metrics were higher than 91% except for sensitivity and MCC for meningioma on the testing dataset. Conclusions: The results from the experiment reveal that the proposed software can be used to detect and diagnose three types of brain tumors. This developed web-based software can be accessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.en_US
dc.identifier.doi10.18521/ktd.889777
dc.identifier.endpage200en_US
dc.identifier.issn1309-3878
dc.identifier.issue2en_US
dc.identifier.startpage192en_US
dc.identifier.trdizinid466962en_US
dc.identifier.urihttps://doi.org/10.18521/ktd.889777
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/466962
dc.identifier.urihttps://hdl.handle.net/11616/92860
dc.identifier.volume13en_US
dc.identifier.wosWOS:000689729500005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherDuzce Univ, Fac Medicineen_US
dc.relation.ispartofKonuralp Tip Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBrain Tumorsen_US
dc.subjectDeep-Learning Strategyen_US
dc.subjectKeras/Auto-Kerasen_US
dc.subjectT1-Weighted Magnetic Resonance Imagingen_US
dc.titleAutomated Classification of Brain Tumors by Deep Learning-Based Models on Magnetic Resonance Images Using a Developed Web-Based Interfaceen_US
dc.typeArticleen_US

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