Locust Genetic Image Processing Classification Model-Based Brain Tumor Classification in MRI Images for Early Diagnosis

Abstract

When analyzing photos considering many criteria, image processing is crucial. The three different forms of image processing techniques that lessen storage space complexity are picture enhancement, restoration, and compression. Segmentation is a crucial stage that plays a crucial role throughout the entire image processing approach. Medical image evaluation frequently employs enhancement methods based on image segmentation. This paper proposed an optimal genetic algorithm-based approach for the brain tumor classification in the MRI images. The proposed mode for the brain tumor classification is stated as the LGO. The proposed LGO model comprises of segmentation and clustering with the Gabor filter. The LGO computes the pixels in the MRI images and estimates the features with the images. Upon the estimated features the classification in the images is performed for the diagnosis of the tumor in the brain. The LGO model achieves a higher classification accuracy of 90 % compared with the conventional classification techniques.