Matches in SemOpenAlex for { <https://semopenalex.org/work/W4315694979> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4315694979 abstract "Recent advances in machine learning have employed deep learning to do several tasks. Deep learning has been used in the health sector to solve complex problems that require human intelligence. Without timely medical attention, the prognosis for patients with brain tumors is dismal. Radiologists are responsible for classifying tumors in radiographic images, which is a complex and time-consuming process that relies solely on their expertise. Modern radiology diagnosis, such as magnetic resonance (MR) scans, is largely subjective, putting patients at risk of damage. Use of Artificial Intelligence (AI) technology in order to avoid making mistakes when diagnosing is important to success. An automated approach for classifying different brain tumor classes in patients using magnetic resonance imaging (MRI) was suggested in this research, which focused on merging deep learning and radionics. We performed our work on three unique datasets with several classes. The proposed technique makes use of a convolutional neural network (CNN) as our deep learning model with the K-fold cross-validation concept in order to perform both binary and multiclass classification on our magnetic resonance imaging (MR) data. We took advantage of the power of CNN architecture in medical imaging. The model was trained and tested on random folded images from the dataset and was able to get an accuracy rate of 100%, 99.86%, and 100% in the corresponding dataset respectively, those are utterly remarkable, to put it mildly." @default.
- W4315694979 created "2023-01-12" @default.
- W4315694979 creator A5013227916 @default.
- W4315694979 creator A5063319179 @default.
- W4315694979 creator A5068820048 @default.
- W4315694979 creator A5077596245 @default.
- W4315694979 creator A5078768739 @default.
- W4315694979 creator A5088956116 @default.
- W4315694979 date "2022-10-27" @default.
- W4315694979 modified "2023-10-14" @default.
- W4315694979 title "Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images" @default.
- W4315694979 cites W2058742048 @default.
- W4315694979 cites W2124527581 @default.
- W4315694979 cites W2809598685 @default.
- W4315694979 cites W2810138651 @default.
- W4315694979 cites W2897188827 @default.
- W4315694979 cites W2947735999 @default.
- W4315694979 cites W2955805844 @default.
- W4315694979 cites W2963356165 @default.
- W4315694979 cites W2972838422 @default.
- W4315694979 cites W3011430986 @default.
- W4315694979 cites W3091923372 @default.
- W4315694979 cites W3097945486 @default.
- W4315694979 cites W3121092655 @default.
- W4315694979 cites W4214738898 @default.
- W4315694979 doi "https://doi.org/10.1109/iceet56468.2022.10007116" @default.
- W4315694979 hasPublicationYear "2022" @default.
- W4315694979 type Work @default.
- W4315694979 citedByCount "3" @default.
- W4315694979 countsByYear W43156949792023 @default.
- W4315694979 crossrefType "proceedings-article" @default.
- W4315694979 hasAuthorship W4315694979A5013227916 @default.
- W4315694979 hasAuthorship W4315694979A5063319179 @default.
- W4315694979 hasAuthorship W4315694979A5068820048 @default.
- W4315694979 hasAuthorship W4315694979A5077596245 @default.
- W4315694979 hasAuthorship W4315694979A5078768739 @default.
- W4315694979 hasAuthorship W4315694979A5088956116 @default.
- W4315694979 hasConcept C108583219 @default.
- W4315694979 hasConcept C111919701 @default.
- W4315694979 hasConcept C115961682 @default.
- W4315694979 hasConcept C119857082 @default.
- W4315694979 hasConcept C12267149 @default.
- W4315694979 hasConcept C126838900 @default.
- W4315694979 hasConcept C143409427 @default.
- W4315694979 hasConcept C153180895 @default.
- W4315694979 hasConcept C154945302 @default.
- W4315694979 hasConcept C31601959 @default.
- W4315694979 hasConcept C41008148 @default.
- W4315694979 hasConcept C50644808 @default.
- W4315694979 hasConcept C66905080 @default.
- W4315694979 hasConcept C71924100 @default.
- W4315694979 hasConcept C75294576 @default.
- W4315694979 hasConcept C81363708 @default.
- W4315694979 hasConcept C98045186 @default.
- W4315694979 hasConceptScore W4315694979C108583219 @default.
- W4315694979 hasConceptScore W4315694979C111919701 @default.
- W4315694979 hasConceptScore W4315694979C115961682 @default.
- W4315694979 hasConceptScore W4315694979C119857082 @default.
- W4315694979 hasConceptScore W4315694979C12267149 @default.
- W4315694979 hasConceptScore W4315694979C126838900 @default.
- W4315694979 hasConceptScore W4315694979C143409427 @default.
- W4315694979 hasConceptScore W4315694979C153180895 @default.
- W4315694979 hasConceptScore W4315694979C154945302 @default.
- W4315694979 hasConceptScore W4315694979C31601959 @default.
- W4315694979 hasConceptScore W4315694979C41008148 @default.
- W4315694979 hasConceptScore W4315694979C50644808 @default.
- W4315694979 hasConceptScore W4315694979C66905080 @default.
- W4315694979 hasConceptScore W4315694979C71924100 @default.
- W4315694979 hasConceptScore W4315694979C75294576 @default.
- W4315694979 hasConceptScore W4315694979C81363708 @default.
- W4315694979 hasConceptScore W4315694979C98045186 @default.
- W4315694979 hasFunder F4320309327 @default.
- W4315694979 hasLocation W43156949791 @default.
- W4315694979 hasOpenAccess W4315694979 @default.
- W4315694979 hasPrimaryLocation W43156949791 @default.
- W4315694979 hasRelatedWork W2731899572 @default.
- W4315694979 hasRelatedWork W2732542196 @default.
- W4315694979 hasRelatedWork W3111570720 @default.
- W4315694979 hasRelatedWork W3156786002 @default.
- W4315694979 hasRelatedWork W4281780675 @default.
- W4315694979 hasRelatedWork W4285149559 @default.
- W4315694979 hasRelatedWork W4293094023 @default.
- W4315694979 hasRelatedWork W4311257506 @default.
- W4315694979 hasRelatedWork W4315694979 @default.
- W4315694979 hasRelatedWork W564581980 @default.
- W4315694979 isParatext "false" @default.
- W4315694979 isRetracted "false" @default.
- W4315694979 workType "article" @default.