Author dc.contributor.author | Wang Kegang | |
Author dc.contributor.author | Band Shahab S. | |
Author dc.contributor.author | Ameri Rasoul | |
Author dc.contributor.author | Biyari Meghdad | |
Author dc.contributor.author | Hai Tao | |
Author dc.contributor.author | Hsu Chung-Chian | |
Author dc.contributor.author | Hadjouni Myriam | |
Author dc.contributor.author | Elmannai Hela | |
Author dc.contributor.author | Chau Kwok-Wing | |
Author dc.contributor.author | Mosavi Amir | |
Availability Date dc.date.accessioned | 2023-02-16T11:24:50Z | |
Availability Date dc.date.available | 2023-02-16T11:24:50Z | |
Release dc.date.issued | 2022 | |
Issn dc.identifier.issn | 1997-003X | |
Issn dc.identifier.issn | 1994-2060 | |
uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/19968 | |
Abstract dc.description.abstract | Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed 23-layers CNN architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed 23 layers CNN architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection). | |
Language dc.language | en | |
Keywords dc.subject | river streamflow | |
Keywords dc.subject | wavelet | |
Keywords dc.subject | machine learning | |
Keywords dc.subject | hybridmodels | |
Keywords dc.subject | estimation | |
Title dc.title | Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow | |
Type dc.type | folyóiratcikk | |
Date Change dc.date.updated | 2023-02-14T14:25:50Z | |
Version dc.description.version | kiadói | |
dc.rights.accessRights | nyílt hozzáférésű | |
dc.description.notes | „A publikáció a Nemzeti Közszolgálati Egyetem 2020. évi Tématerületi Kiválóság Program keretében, a Fenntartható biztonság és társadalmi környezet elnevezésű projekt támogatásával valósult meg, az Innovációs és Technológiai Minisztérium Nemzeti Kutatási, Fejlesztési és Innovációs Alapból nyújtott támogatásával, a Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal által kibocsátott támogatói okirat alapján.” Pályázat sorszáma: NKFIH-1273-6/2020 Támogató: NKFIH | |
Doi ID dc.identifier.doi | 10.1080/19942060.2022.2119281 | |
Discipline Discipline + dc.subject.discipline | Természettudományok | |
dc.subject.sciencebranch | Környezettudományok | |
MTMT ID dc.identifier.mtmt | 33082371 | |
dc.identifier.journalTitle | Engineering Applications Of Computational Fluid Mechanics | |
dc.identifier.journalVolume | 16 | |
dc.identifier.journalIssueNumber | 1 | |
Scope dc.format.page | 1833-1848 | |
Wos ID dc.identifier.wos | 000850454900001 | |
ID Scopus dc.identifier.scopus | 85138036807 | |
dc.identifier.journalAbbreviatedTitle | ENG APPL COMP FLUID | |
Release Date dc.description.issuedate | 2022 | |
Author institution dc.contributor.department | Szoftvertervezés- és Fejlesztés Intézet | |
Author institution dc.contributor.department | Információs Társadalom Kutatóintézet | |
Author institution dc.contributor.department | Informatikai Tudományok Doktori Iskola | |
Author institution dc.contributor.department | Óbudai Egyetem | |
Author institution dc.contributor.department | Biztonságtudományi Doktori Iskola | |
Author institution dc.contributor.department | Felsőbbfokú Tanulmányok Intézete |