Crossover smell agent optimized multilayer perceptron for precise brain tumor classification on MRI images

Abstract

The Brain tumor is considered an unusual growth of cells in the nervous system that restricts the normal functionality of the brain. However, is generated in the skull and pressures the brain which affects the health of a person. So it is essential to detect and classify the brain tumor at an early stage before reaching the severity level. Meanwhile, brain tumor detection is performed based on MRI images which are considered an effective diagnosis system. But the detection and classification using MRI images is obtained as a complex task and cannot show the difference between normal and abnormal cells. So to overcome this issue the Crossover Smell Agent Optimized Multilayer Perception (CSA-MLP) is proposed to perform the exact detection and classification of tumor cells from MRI images. The images are collected from three datasets namely MR, Brain MRI, and Brain tumor datasets and they are preprocessed to remove the unwanted noise. After preprocessing the features of the images are extracted to perform the classification process. Moreover, the Convolutional Neural Network (CNN) classifier is used to classify healthy and unhealthy brain cells. The Multi-Layer Perceptron (MLP) is employed for the classification category that minimized the errors and enhanced the performance of the proposed method. The MLP is integrated with the CSA optimization algorithm to improve classification accuracy. The experimentation results revealed that the proposed method achieved a better accuracy of about 98.56% which enhanced the effectiveness compared to existing methods.