Beyond Boundaries: Leveraging a Layer-Modified DeepTumorNet Paradigm for Multi-Class Diagnostics

Abstract

The paper suggests a novel technique for categorizing brain tissue automatically and identifying tumors using MRI scans. This type of hybrid deep learning incorporates convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. This method processes MRI volumes before processing, extracts topological features, and fine-tunes segmentation boundaries. Macrophages. Additionally, it incorporates a multitask learning architecture and adversarial training. Better accuracy in tumor localization is achieved through reduced segmentation errors compared to this method. This approach has the potential to improve clinical procedures and patient outcomes in neuroimaging applications, with promising implications [1-3, 54].