Hybrid Segmentation Approach for Tumors Detection in Brain Using Machine Learning Algorithms

Abstract

Tumors are most dangerous to humans and cause death when patient not noticed it in the early stages. Edema is one type of brain swelling that consists of toxic particles in the human brain. Especially in the brain, the tumors are identified with magnetic resonance imaging (MRI) scanning. This scanning plays a major role in detecting the area of the affected area in the given input image. Tumors may contain cancer or non-cancerous cells. Many experts have used this MRI report as the primary confirmation of the tumors or edemas as cancer cells. Brain tumor segmentation is a significant task that is used to classify the normal and tumor tissues. In this paper, a hybrid segmentation approach (HSA) is introduced to detect the accurate regions of tumors and edemas to the given brain input image. HSA is the combination of an advanced segmentation model and edge detection technique used to find the state of the tumors or edemas. HSA is applied on the Kaggle brain image dataset consisting of MRI scanning images. Edge detection technique improves the detection of tumor or edema region. The performance of the HSA is compared with various algorithms such as Fully Automatic Heterogeneous Segmentation using support vector machine (FAHS-SVM), SVM with Normal Segmentation, etc. Performance of proposed work is calculated using mean square error (MSE), peak signal noise ratio (PSNR), and accuracy. The proposed approach achieved better performance by improving accuracy.