Alzheimer's detection using various feature extraction approaches using a multimodal multi‐class deep learning model

• Published in: International journal of imaging systems and technology Vol. 33; no. 2; pp. 588 - 609
• Main Authors: Goenka, Nitika, Tiwari, Shamik
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2023; Wiley Subscription Services, Inc
• Subjects: 18F‐AV45 PET; Accuracy; Algorithms; Alzheimer's disease; Classification; Deep learning; Feature extraction; Interpolation; Machine learning; Magnetic resonance imaging; Medical imaging; multi‐modality; neuroimaging biomarker; Patches (structures); patch‐based; Positron emission; slice‐based; Slicing; subject‐level; T1w‐sMRI; volumetric convnet
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22813

Alzheimer's disease is a chronic brain condition that takes a toll on memory and potential to do even the most basic tasks. With no specific solution viable at this time, it's critical to pinpoint the start of Alzheimer's disease so that necessary steps may be initiated to limit its progression. We used three distinct neuroanatomical computational methodologies namely 3D‐Subject, 3D‐Patches, and 3D‐Slices to construct a multimodal multi‐class deep learning model for three class and two class Alzheimer's classification using T1w‐MRI and AV‐45 PET scans obtained from ADNI database. Further, patches of various sizes were created using the patch‐extraction algorithm designed with torch package leading to separate datasets of patch size 32, 40, 48, 56, 64, 72, 80, and 88. In addition, Slices were produced from images using either uniform slicing, subset slicing, or interpolation zoom approaches then joined back to form a 3D image of varying depth (8,16,24,32,40,48,56, and 64) for the Slice‐based technique. Using T1w‐MRI and AV45‐PET scans, our multimodal multi‐class Ensembled Volumetric ConvNet framework obtained 93.01% accuracy for AD versus NC versus MCI (highest accuracy achieved using multi‐modalities as per our knowledge). The 3D‐Subject‐based neuroanatomy computation approach achieved 93.01% classification accuracy and it overruled Patch‐based approach which achieved 89.55% accuracy and Slice‐Based approach that achieved 89.37% accuracy. Using a 3D‐Patch‐based feature extraction technique, it was discovered that patches of greater size (80, 88) had accuracy over 89%, while medium‐sized patches (56, 64, and 72) had accuracy ranging from 83 to 88%, and small‐sized patches (32, 40, and 48) had the least accuracy ranging from 57 to 80%. From the three independent algorithms created for 3D‐Slice‐based neuroanatomy computational approach, the interpolation zoom technique outperformed uniform slicing and subset slicing, obtaining 89.37% accuracy over 88.35% and 82.83%, respectively. Link to GitHub code: https://github.com/ngoenka04/Alzheimer-Detection.