DMSENet: Deep multi-modal squeeze and excitation network for the diagnosis of Alzheimer's disease

Abstract

ABSTRACT Alzheimer's Disease (AD) is an incurable neurodegenerative disorder that affects millions of older people worldwide. Compared to conventional methods, neuroimaging modalities along with the machine learning techniques detect the onset of AD more successfully. It has been established that multimodal classification provides better accuracy than single modal classification. Exploring the synergy between several multimodal neuroimages remains challenging due to the lack of available fusion techniques. The proposed Deep Multimodal Squeeze and Excitation network (DMSENet) uses the ResNet Squeeze and Excitation (SE) block to extract relevant features from MRI and PET images. Using the hierarchical fusion method, the extracted features are fused; subsequently, using the fused Feature Map (FM), the ResNet SE block retrieves additional higher-level and lower-level features. Hierarchical fusion methodology ensures the efficiency of Multimodal Fusion (MMF); the Attention Model(AM) then assigns the fusion ratio automatically by prioritizing the multimodal data. Moreover, the depth and efficiency of the attention network are ensured by the combination of identity mapping and Residual Block. In the DMSENet, both higher-level and lower-level features could be utilized simultaneously by employing both early and late fusion methodologies. The suggested framework is investigated using the ADNI dataset, providing greater precision than state-of-the-art methods.