End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer’s Diagnosis

Deevyankar Agarwal, Manuel Alvaro Berbis, Teodoro Martín-Noguerol, Antonio Luna, Sara Carmen Parrado Garcia and Isabel de la Torre-Díez
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Deevyankar Agarwal: Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Manuel Alvaro Berbis: Hospital San Juan de Dios, HT Medica, Avda Brillante 106, 14012 Córdoba, Spain
Teodoro Martín-Noguerol: MRI Unit, Radiology Department, HT Medica, Carmelo Torres No. 2, 23007 Jaén, Spain
Antonio Luna: MRI Unit, Radiology Department, HT Medica, Carmelo Torres No. 2, 23007 Jaén, Spain
Sara Carmen Parrado Garcia: Radiodiagnosis Service, University Clinical Hospital of Valladolid, SACYL, Av. Ramón y Cajal, 3, 47003 Valladolid, Spain
Isabel de la Torre-Díez: Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain

Mathematics, 2022, vol. 10, issue 15, 1-28

Abstract: This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings.

Keywords: Alzheimer’s disease; CNN; deep learning; end-to-end learning; MCI; MRI; neuroimaging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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