Title:RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network
Volume: 22
Issue: 9
Author(s): Amsavalli Kannan, Kanaga Suba Raja Subramanian*Sudha Suresh
Affiliation:
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India
Keywords:
Alzheimer's disease, non-local means (NLM) filter, deep learning, ResNet-LSTM model, clinical diagnosis, SegNet.
Abstract:
Introduction: Alzheimer's disease (AD) is a leading cause of death, making early detection
critical to improve survival rates. Conventional manual techniques struggle with early diagnosis
due to the brain's complex structure, necessitating the use of dependable deep learning (DL)
methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for
detecting AD utilizing MRI images.
Methods: The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce
noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter
(WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated
and classified into Normal, MCI, and AD categories using an Inception V3-based classifier.
Additionally, SegNet was employed for abnormal brain region segmentation.
Results: The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of
95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense-
Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy
by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.
Discussion: The integration of spatial-temporal feature extraction, hybrid classification, and deep
segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its
robustness, and its performance exceeded that of existing models on the ADNI dataset. However,
there are potential limitations related to dataset bias and limited generalizability due to uniform
imaging conditions.
Conclusion: The proposed RESIGN model demonstrates significant improvement in early AD detection
through robust feature extraction and classification by offering a reliable tool for clinical
diagnosis.
