Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

Abstract

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier.