| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| B V Prakash, A Rajiv Kannan, N Santhiyakumari, S Kumarganesh, D Siva Sundhara Raja, J Jasmine Hephzipah, K MartinSagayam, Marc Pomplun, Hien Dan. Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform. Scientific reports. vol 13. issue 1. 2023-09-04. PMID:37666922. |
meningioma brain tumor detection and classification using hybrid cnn method and ridgelet transform. |
2023-09-04 |
2023-09-07 |
Not clear |
| Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan, Heba G Mohame. Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning. Brain sciences. vol 13. issue 4. 2023-05-16. PMID:37190567. |
this study presents a novel cnn algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. |
2023-05-16 |
2023-08-14 |
Not clear |
| Xin Ma, Yajing Zhao, Yiping Lu, Peng Li, Xuanxuan Li, Nan Mei, Jiajun Wang, Daoying Geng, Lingxiao Zhao, Bo Yi. A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images. Computers in biology and medicine. vol 151. issue Pt A. 2022-11-14. PMID:36375416. |
a dual-branch hybrid dilated cnn model for the ai-assisted segmentation of meningiomas in mr images. |
2022-11-14 |
2023-08-14 |
Not clear |
| John Nisha Anita, Sujatha Kumara. A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation. Journal of cancer prevention. vol 27. issue 3. 2022-10-19. PMID:36258715. |
in this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (cnn) classification approach. |
2022-10-19 |
2023-08-14 |
Not clear |
| Ejaz Ul Haq, Huang Jianjun, Xu Huarong, Kang Li, Lifen Wen. A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI. Computational and mathematical methods in medicine. vol 2022. 2022-08-29. PMID:36035291. |
the experimental results validate that the proposed deep cnn and svm-rbf classifier achieved an accuracy of 98.3% and a dice similarity coefficient (dsc) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on figshare dataset, it achieved an accuracy of 98.0% and a dsc of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. |
2022-08-29 |
2023-08-14 |
human |
| Loveleen Gaur, Mohan Bhandari, Tanvi Razdan, Saurav Mallik, Zhongming Zha. Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data. Frontiers in genetics. vol 13. 2022-04-01. PMID:35360838. |
consequently, we propose an explanation-driven dl model by utilising a convolutional neural network (cnn), local interpretable model-agnostic explanation (lime), and shapley additive explanation (shap) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an mri image dataset. |
2022-04-01 |
2023-08-13 |
Not clear |
| April Vassantachart, Yufeng Cao, Michael Gribble, Samuel Guzman, Jason C Ye, Kyle Hurth, Anna Mathew, Gabriel Zada, Zhaoyang Fan, Eric L Chang, Wensha Yan. Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network. Scientific reports. vol 12. issue 1. 2022-03-10. PMID:35264655. |
a novel asymmetric cnn with two differently weighted encoding paths was developed for successful automated meningioma grade classification. |
2022-03-10 |
2023-08-13 |
Not clear |
| Akila Gurunathan, Batri Krishna. A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor. Brain imaging and behavior. 2022-01-20. PMID:35048264. |
a supervised cnn deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. |
2022-01-20 |
2023-08-13 |
Not clear |
| Tommaso Banzato, Marco Bernardini, Giunio B Cherubini, Alessandro Zott. A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images. BMC veterinary research. vol 14. issue 1. 2018-11-27. PMID:30348148. |
the aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (cnn, googlenet) in discriminating between meningiomas and gliomas in pre- and post-contrast t1 images and t2 images; 2) to develop an image classifier, based on the combination of cnn and mri sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma. |
2018-11-27 |
2023-08-13 |
Not clear |