Paper Details
Exploring Advanced Methods for Brain Tumor Detection and Segmentation with a Focus on the EfficientNetB3 Architecture
Authors
Rashmi Jaiswal, Prof. Vikas Kamle
Abstract
Researchers have successfully used the EfficientNetB3 model as a state of the art for medical image analysis, notably in detecting and segmenting brain tumors. In this research, we present the results of experiments that using MRI images for tumor identification with two different ML models. We achieved a top accuracy of 91.65% with RBF, Linear and Polygonal kernels using the conventional methods Similar to HD-SBM Data: However, despite this best of all is EfficientNetB3 model which got the accuracy rate above 98%. A remarkable achievement that sheds light on how well the model can deal with all of the challenges required for finding brain tumors. Furthermore, its potential effectiveness is providing greater scope for clinical application than hitherto and has the capacity to change early diagnosis as well as potentially personalised management.
Keywords
Brain Tumor Segmentation, EfficientNetB3, Medical Imaging, Deep Learning, Tumor Detection
Citation
Exploring Advanced Methods for Brain Tumor Detection and Segmentation with a Focus on the EfficientNetB3 Architecture. Rashmi Jaiswal, Prof. Vikas Kamle. 2024. IJIRCT, Volume 10, Issue 4. Pages 1-7. https://www.ijirct.org/viewPaper.php?paperId=2408066