CirMNet: A Shape-Based Hybrid Feature Extraction Technique Using CNN and CMSMD for Alzheimer’s MRI Classification

Abstract

This research introduces a novel approach called Circular Mesh Network (CirMNet), a shape-based hybrid feature extraction technique. It also proposes innovative Fractal Dimension (FD) and statistical feature extraction techniques for vertically symmetrical images. Convolutional Neural Networks (CNNs) have gained widespread popularity across various domains, including the interpretation of medical images. CNNs excel at extracting prominent features in the initial layers and progressively learn to capture more complex features as they advance. However, the pooling and striding operations involved in CNNs can lead to a loss of spatial and structural details in the image because CNNs require a mechanism to preserve the internal representation and describe the intricate relationships between image components or pixels. Circular Mesh-based Shape and Margin Descriptor (CMSMD) focuses on extracting structural, statistical, and property-based features. However, it does not encompass features such as texture or color. The objective of CirMNet is to leverage the strengths of both CNNs and CMSMD, and to mitigate their respective weaknesses. Structural and texture features were generated from CirMNet, and its performance was evaluated for the diagnosis of neurodegenerative disorders, particularly Alzheimer’s Disease (AD). The model can easily identify the permanent shrinkage and destruction of brain cells in MRIs of patients and exhibited a notable accuracy of 97.34% in classifying various stages of AD, encompassing Control, Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI). This achievement represents a substantial improvement over the existing state-of-the-art methods in the domain.

Keywords

• Alzheimer’s disease
• magnetic resonance imaging
• convolutional neural network
• structural shapes
• feature extraction