Early Alzheimer's Disease Detection Through YOLO-Based Detection of Hippocampus Region in MRI Images

Junaidul Islam, Elvin Nur Furqon, Isack Farady, Chi Wen Lung, Chih Yang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Magnetic Resonance Imaging (MRI) is currently one of the most promising tools for detecting Alzheimer's disease (AD), as it allows for the analysis of brain regions affected by the disease, such as the hippocampus. However, the availability of labeled datasets for hippocampus regions in MRI images is limited, and manually annotating such images can be expensive and time-consuming task, particularly for large datasets. To overcome this challenge, we propose a deep learning approach that leverages object detection models to automatically identify the hippocampus region in MRI images. In our study, we employed various YOLO-based models to detect and classify the AD classes based on the hippocampus region in MRI images. We specifically selected the latest state-of-the-art YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv6, and YOLOv7. Our approach shows potential for improving the early detection of Alzheimer's disease using deep learning and object detection and may be useful for developing automated diagnostic tools for clinical applications. We conducted experiments in two scenarios to validate our proposed idea: one-class detection and two-class detection. One-class detection detects a specific class based on the appearance of the hippocampus region, while two-class detection aims to detect and classify the AD level based on the hippocampus. Our preliminary results demonstrate that YOLO variants are viable for accurately detecting the hippocampus region in MRI images, with potential applications in hippocampus detection.

Original languageEnglish
Title of host publicationProceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-35
Number of pages4
ISBN (Electronic)9798350301953
DOIs
StatePublished - 2023
Event6th International Symposium on Computer, Consumer and Control, IS3C 2023 - Taichung City, Taiwan
Duration: 30 Jun 20233 Jul 2023

Publication series

NameProceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023

Conference

Conference6th International Symposium on Computer, Consumer and Control, IS3C 2023
Country/TerritoryTaiwan
CityTaichung City
Period30/06/233/07/23

Keywords

  • Alzheimer's disease detection
  • Magnetic Resonance Imaging
  • YOLO
  • automatic labeling
  • hippocampus region

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