Discrimination of Alzheimer’s diseases based on combination of generalized Hadamard difference and deep learning features

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to a decline in memory and cognitive functions. An early and accurate diagnosis is critical for effective management and treatment also tend to lack sufficient accuracy. Accurate and early discrimination of Alzheimer’s disease (AD) remains a critical challenge in medical imaging and computational neuroscience. Traditional diagnostic approaches, such as clinical assessments and neuroimaging, are often subjective and labor-intensive work. Recently, convolutional neural networks (CNNs) and handcrafted feature extraction techniques have shown promising results for automated AD classification. In order to improve AD detection accuracy, this study suggests a novel and efficient hybrid feature extraction method to address the accurate detection of Alzheimer’s disease (AD). This method combines deep feature representations taken from customized CNN with the Generalized Hadamard Difference (GHD) operator, a mathematical technique that is intended to capture subtle structural variations. This integrated approach leverages the complementary strengths of handcrafted and learned features to better characterize the complex patterns associated with AD progression. The publicly accessible OASIS-1 MRI dataset was employed to rigorously evaluate the proposed method, consisting of high-resolution T1-weighted brain images from cognitively normal and impaired subjects. Classification was performed using a Support Vector Machine (SVM) classifier, yielding an impressive overall accuracy of 99.50 %, surpassing many existing state-of-the-art methods. The results highlight that incorporating GHD with deep features improved the method’s ability to detect early and subtle manifestations of AD.