Brain Tumors Detection Using Deep Learning (SGO and SE Algorithm)

Abstract

Brain abnormality is the harsh illness among humans and is usually diagnosed with medical imaging procedures. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and Diffusion-Weighted (DW) modalities. The combination of the Social- Group-Optimization (SGO) and Shannon’s Entropy (SE) supported Multi-threshold is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES2015 and BRATS (2013 and 2015), and also clinical MRI mages obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with related segmentation trials employed. The performance of the ANFIS is validated with other classifiers, such as Decision-Tree, Random-Forest and KNN. The ANFIS classifier obtained an accuracy of 97.04% on the used DW modality and accuracy of 96.67% on the Flair MR images.