Switchable Normalization Based Faster RCNN for MRI Brain Tumor Segmentation

In recent decades, brain tumors have emerged as a serious neurological disorder that often leads to death. Hence, Brain Tumor Segmentation (BTS) is significant to enable the visualization, classification, and delineation of tumor regions in Magnetic Resonance Imaging (MRI). However, BTS remains a challenging task because of noise, non-uniform object texture, diverse image content and clustered objects. To address these challenges, a novel model is implemented in this research. The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN, which effectively captures the fine-grained tumor features to enhance segmentation precision. MRI images are initially acquired from three online datasets: Dataset 1—Brain Tumor Segmentation (BraTS) 2018, Dataset 2—BraTS 2019, and Dataset 3—BraTS 2020. Subsequently, the Switchable Normalization-based Faster Regions with Convolutional Neural Networks (SNFRC) model is proposed for improved BTS in MRI images. In the proposed model, Switchable Normalization is integrated into the conventional architecture, enhancing generalization capability and reducing overfitting to unseen image data, which is essential due to the typically limited size of available datasets. The network depth is increased to obtain discriminative semantic features that improve segmentation performance. Specifically, Switchable Normalization captures the diverse feature representations from the brain images. The Faster R-CNN model develops end-to-end training and effective regional proposal generation, with an enhanced training stability using Switchable Normalization, to perform an effective segmentation in MRI images. From the experimental results, the proposed model attains segmentation accuracies of 99.41%, 98.12%, and 96.71% on Datasets 1, 2, and 3, respectively, outperforming conventional deep learning models used for BTS.