Abstract
Alzheimer’s Disease is among the most common causes of death worldwide, and it is expected to have a greater impact in the years to come. Currently, there are no effective means to halt its progression, but researchers are actively exploring prevention, diagnosis, prognosis, and treatment options to find better solutions in each domain. Notably, extensive studies have shown that early detection plays a crucial role in developing more accurate prognoses and appropriate treatments. Presently, the primary diagnostic tests employed in this regard are images derived from Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI). PETs are mainly used for obtaining functional information from the produced image, while MRIs reveal structural impairment. As artificial intelligence (AI) is increasingly used to support diagnostics for the development of ever more performing classifiers, in this paper we intend to show a study related to a Multimodal Deep Learning (MDL) approach that could guarantee better classification performance, integrating MRI structural information with PET functional information. The classifiers we are going to introduce is based on 3D Deep Convolutional Neural Networks (CNN). Here we will focus on Early Fusion (EF) and Late Fusion (LF) approaches on unbalanced and incomplete datasets exported from the Californian ADNI project.
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De Simone, A., Sansone, C. (2024). A Multimodal Deep Learning Based Approach for Alzheimer’s Disease Diagnosis. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham. https://doi.org/10.1007/978-3-031-51026-7_12
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