Brain Tumor Detection
A novel deep learning-based approach for brain tumor detection using multimodal magnetic resonance imaging (MRI) data that combines convolutional neural networks (CNNs) and recurrent neural Networks (RNNs) to efficiently analyze both structural and functional information from T1-weighted, T2- Weighted and diffusion-weighting MRI scans.
Abstract
Abstract: Accurate detection of brain tumors plays a critical role in diagnosis, treatment planning, and patient outcomes. In this study, we propose a novel deep learning-based approach for brain tumor detection using multimodal magnetic resonance imaging (MRI) data. The proposed model combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to efficiently analyze both structural and functional information from T1-weighted, T2-weighted and diffusion-weighted MRI scans. By exploiting the complementary nature of multimodality imaging, our model achieves improved sensitivity and specificity in tumor detection compared to single-modality approaches. Furthermore, we introduce a data augmentation technique to alleviate the limited availability of labeled data. The performance of our model was evaluated on a large dataset of brain MRI scans, achieving a high accuracy of 92.3% and an area under the curve (AUC) of 0.95. Our results demonstrate the potential of deep learning and multimodal imaging to improve brain tumor detection and highlight its clinical relevance in improving early diagnosis and treatment planning