Efficient Brain Tumor Detection and Segmentation Using DN-MRCNN With Enhanced Imaging Technique.

Abstract

This article proposes a method called DenseNet 121-Mask R-CNN (DN-MRCNN) for the detection and segmentation of brain tumors. The main objective is to reduce the execution time and accurately locate and segment the tumor, including its subareas. The input images undergo preprocessing techniques such as median filtering and Gaussian filtering to reduce noise and artifacts, as well as improve image quality. Histogram equalization is used to enhance the tumor regions, and image augmentation is employed to improve the model's diversity and robustness. To capture important patterns, a gated axial self-attention layer is added to the DenseNet 121 model, allowing for increased attention during the analysis of the input images. For accurate segmentation, boundary boxes are generated using a Regional Proposal Network with anchor customization. Post-processing techniques, specifically nonmaximum suppression, are performed to neglect redundant bounding boxes caused by overlapping regions. The Mask R-CNN model is used to accurately detect and segment the entire tumor (WT), tumor core (TC), and enhancing tumor (ET). The proposed model is evaluated using the BraTS 2019 dataset, the UCSF-PDGM dataset, and the UPENN-GBM dataset, which are commonly used for brain tumor detection and segmentation.