Convolutional neural networks to classify alzheimer’s disease severity based on SPECT images: A comparative study

Wei-Chih Lien, Chung-Hsing Yeh, Chun-Yang Chang, Chien-Hsiang Chang, Wei-Ming Wang, Chien-Hsu Chen, Yang-Cheng Lin

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer’s disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni’s post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images.

Original languageEnglish
Article number2218
Number of pages14
JournalJournal of Clinical Medicine
Volume12
Issue number6
DOIs
Publication statusPublished - 13 Mar 2023

Keywords

  • Alzheimer’s disease (AD)
  • convolutional neural network (CNN)
  • image recognition
  • single-photon emission computed tomography (SPECT)
  • transfer learning

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