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Updated: Sep 18, 2025

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RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network.

K Amsavalli1, S Kanaga Suba Raja2, S Sudha3,4

  • 1Department of Artificial Intelligence and Data Science, Easwari Engineering College, Chennai, Tamil Nadu, India.

Current Alzheimer Research
|June 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the RESIGN deep learning model for early Alzheimer's disease (AD) detection using MRI scans. RESIGN achieves high accuracy, offering a reliable tool for clinical diagnosis and improved patient outcomes.

Keywords:
Alzheimer's diseaseResNet-LSTM modelSegNet.clinical diagnosisdeep learningnon-local means (NLM) filter

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Alzheimer's disease (AD) poses a significant health challenge, necessitating early detection for improved patient survival rates.
  • Traditional diagnostic methods face limitations in accurately identifying early-stage AD due to the brain's complexity.
  • Deep learning (DL) offers a promising avenue for enhancing the accuracy and efficiency of AD diagnosis.

Purpose of the Study:

  • To propose and evaluate a novel deep learning model, RESIGN (Res-InceptionSeg), for the early detection of Alzheimer's disease using MRI images.
  • To leverage a hybrid approach combining segmentation and classification for robust AD diagnosis.
  • To assess the performance of RESIGN against existing deep learning models in identifying AD, Mild Cognitive Impairment (MCI), and normal cognitive states.

Main Methods:

  • MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise.
  • A ResNet-LSTM model was employed for feature extraction from White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF).
  • An Inception V3-based classifier and SegNet were utilized for classification and abnormal brain region segmentation, respectively.

Main Results:

  • The RESIGN model achieved a high accuracy of 99.46%, with a specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%.
  • RESIGN demonstrated superior performance compared to several established deep learning models including ResNet, AlexNet, DenseNet, and LSTM.
  • The model showed significant accuracy improvements over ResNet18, CLSTM, VGG19, and CNN.

Conclusions:

  • The RESIGN model's integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation provides a highly reliable method for early AD detection.
  • Robustness was confirmed through 5-fold cross-validation, with performance exceeding existing models on the ADNI dataset.
  • RESIGN offers a dependable tool for clinical diagnosis, though potential dataset bias and generalizability limitations warrant further investigation.