Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification
Source abstract
Abstract
Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pitu... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · Brain Tumor Detection and Classification · Medical Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. Computer-aided early melanoma brain-tumor detection using deep-learning approach10.3390/biomedicines11010184 ↗
2. Artificial intelligence approach for early detection of brain tumors using MRI images10.3390/app13063808 ↗
3. A systematic literature review of deep and machine learning algorithms in brain tumor and meta-analysis
4. Deep learning based brain tumor segmentation: A survey10.1007/s40747-022-00815-5 ↗
5. Brain tumor detection based on deep learning approaches and magnetic resonance imaging10.3390/cancers15164172 ↗
6. Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation10.1016/j.inffus.2022.12.013 ↗
7. A neural network framework for predicting the tissue-of-origin of 15 common cancer types based on RNA-Seq data10.3389/fbioe.2020.00737 ↗
8. Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model
9. A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation
10. Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network10.1016/j.ejro.2023.100484 ↗
11. Hybrid techniques of analyzing MRI images for early diagnosis of brain tumours based on hybrid features10.3390/pr11010212 ↗
12. An intelligent hybrid approach for brain tumor detection
13. Deep neural network correlation learning mechanism for CT brain tumor detection10.1007/s00521-021-05841-x ↗
14. Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data10.1016/j.bspc.2022.104424 ↗
15. Role of ensemble deep learning for brain tumor classification in multiple magnetic resonance imaging sequence data10.3390/diagnostics13030481 ↗
16. A lightweight deep learning based microwave brain image network model for brain tumor classification using reconstructed microwave brain (RMB) images10.3390/bios13020238 ↗
17. MobileNetV1-based deep learning model for accurate brain tumor classification
18. Brain tumor segmentation in multimodal MRI images using novel LSIS operator and deep learning10.1007/s12652-022-03773-5 ↗
19. Bridged-U-Net-ASPP-EVO and deep learning optimization for brain tumor segmentation10.3390/diagnostics13162633 ↗
20. High accuracy brain tumor classification with EfficientNet and magnetic resonance images
21. Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging10.1016/j.heliyon.2024.e25468 ↗
22. J. Cheng, “Brain tumor dataset,” figshare, 2017. Accessed: Dec. 05, 2023. [Online]. Available: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427
23. S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, “Brain Tumor Classification (MRI),” Kaggle, 2020. Accessed: Dec. 10, 2023. [Online]. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
24. Hamada, “Brain tumor detection,” Kaggle, 2020. Accessed: Jan. 10, 2024. [Online]. Available: https://kaggle.com/datasets/ahmedhamada0/brain-tumor-detection?select=no
25. Evaluating cancer-related biomarkers based on pathological images: A systematic review
26. Classification of brain tumor images using deep learning methods
27. Brain tumor classification using VGG 16, ResNet50, and Inception V3 transfer learning models
28. HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection10.1016/j.bspc.2023.104737 ↗
29. Explanation-driven deep learning model for prediction of brain tumour status using MRI image data10.3389/fgene.2022.822666 ↗
30. Construction of VGG16 convolution neural network (VGG16_CNN) classifier with NestNet-based segmentation paradigm for brain metastasis classification
31. Brain tumor identification using data augmentation and transfer learning approach
32. A deep analysis of brain tumor detection from mr images using deep learning networks10.3390/a16040176 ↗
33. Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images10.1007/s11063-022-11014-1 ↗