Locust-based genetic classifier for abnormality identification in brain

This study investigates the loctus-based genetic classifier for abnormality identification in the brain. In medical care, clinical professionals have to spend a lot of time extracting, identifying, and segmenting the afflicted region from magnetic resonance brain images. Utilizing computer-aided approaches is crucial to overcome this restriction. Henceforth, this paper proposes an efficient classifier for diagnosing abnormalities in human brain using magnetic resonance images (MRI). The focus is on improving the accuracy and efficiency of medical image segmentation, specifically for brain tumours, to assist clinical professionals in early disease detection. In order to improve the image's quality through noise reduction, the Gaussian filter is employed during the pre-processing stage. The proposed tumour segmentation is based on the Otsu algorithm, and the gray-level co-occurrence matrix (GLCM) is used to extract the relevant features. In this work, the locust-based genetic classifier plays a crucial role in early brain disease identification and pinpointing the precise location of the damaged area. Accuracy, sensitivity, and specificity have been used to analyze and validate the outcomes of the proposed technique. The current study's findings indicate accurate prediction of brain abnormality and have a 98.9% accuracy rate, 97.2% specificity, and 96% sensitivity. The study presents a reliable and efficient method for diagnosing brain abnormalities using MRI. The combination of the proposed approaches significantly enhances the segmentation and classification processes, leading to high diagnostic accuracy. This approach offers practical benefits for clinical professionals by reducing the time and effort required for diagnosing brain abnormalities.