Brain tumour segmentation in MRI using fuzzy deformable fusion model with Dolphin-SCA

Abstract

It is evident that when the human brain stops functioning for a small period of time, it will lead to death. As a result, dealing with brain disorders should be done early and properly. A brain tumour is one of the most serious brain illnesses. The development of tumours can be detected using Magnetic Resonance Imaging (MRI). However, because an MRI image has loud noise, it can be hard to diagnose a tumour. The diagnosis process is slow, yet illness necessitates prompt and accurate medical attention in order for patients to survive. One of the solutions for tumour diagnosis is to employ MRI brain picture segmentation. In this designed model, MRI of the brain is collected and pre-processed with Non-Local Means (NLM) to reduce noise from captured raw data. This pre-processed image is first segmented with Region of Interest (ROI) for identifying regions of interest and then with a fusion deformable fuzzy system, which combines fuzzy C-means (FCM) and deformable systems. By analyzing the fitness value of α and β constants, segmented pictures from models are fused using the Dolphin Sine Cosine Algorithm (SCA) method to combine the model results. The integrated output from the algorithm is classified with the deep Convolutional Neural Network (CNN) classifier. The created model experimental findings are analyzed and compared to current methodologies. The proposed model performance measures are 0.90, 0.89, 0.88, and 0.10 in terms of selectivity, precision, accuracy and errors. As a result, when compared to previous strategies, the proposed approach outperforms them.

Keywords

• dolphin-sca
• fcm
• deformable model
• roi
• nlm
• tumour segmentation