Classification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net

References (10)

1. 1 U-Net: Convolutional Networks for Biomedical Image Segmentation [DOI]
2. 2 Machine Learning for Medical Imaging [DOI]
3. 3 Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition [DOI]
4. 4 Machine learning for medical imaging: methodological failures and recommendations for the future [DOI]
5. 5 Error and discrepancy in radiology: inevitable or avoidable? [DOI]
6. 6 Introduction to machine and deep learning for medical physicists [DOI]
7. 7 Enhanced Skin Condition Prediction Through Machine Learning Using Dynamic Training and Testing Augmentation [DOI]
8. 8 Multi-Scale Network for Thoracic Organs Segmentation [DOI]
9. 9 3D Brain Tumors and Internal Brain Structures Segmentation in MR Images [DOI]
10. 10 Proceedings of the 24th international conference on Machine learning [DOI]

Cited by 17

Showing 10 of 17 known citations — external sources report more than can currently be individually listed.

Related research

• Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
• Prediction of Radiotherapy Dose Distribution for Glioblastoma Using Convolutional Neural Network Model — shares topic coverage
• A Systematic Literature Review of Machine Learning Methods in Healthcare — shares topic coverage
• Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
• Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage