AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer's disease through multiple neuroimaging computational approaches. (April 2022)

• Record Type: Journal Article
• Authors: Goenka, Nitika; Tiwari, Shamik
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Alzheimer -- Brain imaging -- Convolutional neural network -- T1w MRI -- Neuroimaging; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103500
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We looked at contemporary status in Alzheimer's categorization using ConvNet & T1w MRI. A method for analysing Alzheimer three-class categorization with the maximum accuracy and binary classification. First study to examine the performance of three neuroimaging computational techniques in a systematic fashion (3D subject-level, 3D patch-based and 3D slice-based). Three different Slice Based approaches used (Subset slice method, uniform slice method, Interpolation zoom method). Classification accuracy of different patches ranging in size from small to medium to huge for 3D patch-based approach. Abstract: Alzheimer's disease is a degenerative neurological disease that causes a loss of cognitive skills and has no known treatment. Alzheimer's disease (AD) must be detected early, before symptoms appear, in order to be treated effectively. In this study, we used a deep learning approach called a convolutional neural network to classify Alzheimer's disease into three categories using a neuroimaging biomarker called T1w-MRI. Our research is the first to look at the results of three neuroimaging computational approaches in a systematic way (3D subject-level, 3D patch-based and slice-based). To show Alzheimer detection using deep convolutional neural networks, three distinct Slice Based methods are used (Subset selection method, uniform selection method, Interpolation zoom method). For 3D patch-based approaches, we investigated the classification accuracy of distinct non-overlapping patches ranging in size from small to medium to large (from 32, 40, 48, 56, 64, 72, 80, till 88). Our findings revealed that 1) our 3-class classification model performed best, with 98.3 percent accuracy percent (highest accuracy obtained until now as per our best knowledge); 2) The 3D Subject-level approach was the most efficient, followed by 3D-patch-based and then Slice-based approaches, with classification accuracy of 98.26 percent, 97.48 percent, and 95.40 percent, respectively; and 3) The same network had the most accuracy for bigger patches (size 72, 80, 88), followed by medium-sized (size 56, 64) to tiny patches (size 32, 40, 48). Large patches had a classification accuracy of 97.48 percent, while medium patches had a classification accuracy of 96.62 percent, and small patches had an accuracy of 86.49 percent. 4)) Even slice selection and interpolation selection exceeded subset slice selection with three-class classification accuracy of 95.37 percent and 94.57 percent, respectively, compared to 92.57 percent for subset slice selection.