EBTAG: explainable brain tumor segmentation with EfficientNetV2, attention mechanism, and Grad-CAM in clinical and public datasets
TL;DR
A novel brain tumor segmentation method that combines a two-path UNet architecture with an upgraded EfficientNetV2 model due to an attention mechanism is provided, which handles 3D MRI data efficiently and increases segmentation accuracy.
Abstract
Automated brain tumor segmentation provides precise evaluations of tumor dimensions and progression across time, which is beneficial for longitudinal studies and treatment oversight. This study provides a novel brain tumor segmentation method that combines a two-path UNet architecture with an upgraded EfficientNetV2 model due to an attention mechanism. By utilizing the advantages of both designs, our technique handles 3D MRI data efficiently and increases segmentation accuracy. By using two distinct encoding pathways, the UNet model is improved and can extract a greater variety of information from MRI images. The pretrained EfficientNetV2 model is also included to further improve the model’s capabilities. By including an attention mechanism, the model is able to concentrate on pertinent features, which further enhances segmentation performance. Using the BraTS 2020 dataset and the clinical dataset, we assessed the performance of the two-path UNet with EfficientNetV2B0 and EfficientNetV2S backbones. The real-clinical dataset comprises MRI scans from 99 patients acquired at Ankara Bilkent City Hospital. The findings showed that particular input combinations produced superior segmentation results, and some models performed exceptionally well at correctly categorizing various tumor kinds. This emphasizes how these models may be useful in clinical settings and how crucial it is to choose the right input configurations and model backbones to get the best segmentation results. Furthermore, the Grad-CAM technique improved the models’ reliability and interpretability, providing crucial insights into their decision-making processes.