A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans

Abstract

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L-Net and W-Net deep learning models. The proposed cascaded model leverages the strengths of U-Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L-Net excels in capturing the mask, while the W-Net focuses on fine-grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi-modal MRI images. The experimental results demonstrate that the cascaded L-Net and W-Net model consistently outperforms individual models and other state-of-the-art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature.