Alzheimer’s disease detection using residual neural network with LSTM hybrid deep learning models

Article type: Research Article

Authors: Vidhya, R.^a | Banavath, Dhanalaxmi^b | Kayalvili, S.^c | Naidu, Swarna Mahesh^d | Charles Prabu, V.^e | Sugumar, D.^f | Hemalatha, R.^g | Vimal, S.^h | Vidhya, R.G.^{i; *}

Affiliations: [a] Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India | [b] Department of Electronics and Communication Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India | [c] Department of Artificial Intelligence, Kongu Engineering College, Perundurai, Tamilnadu, India | [d] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [e] Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India | [f] Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore Tamilnadu, India | [g] Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Tamilnadu, India | [h] Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India | [i] Department of Electronics and Communication Engineering, HKBK College of Engineering, Karnataka, Bangalore, India

Correspondence: [*] Corresponding author. R.G. Vidhya, Department of Electronics and Communication Engineering, HKBK College of Engineering, Karnataka, Bangalore, 560045, India. E-mail: vidhyar.ec@hkbk.edu.in.

Abstract: Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer’s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer’s disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes.

Keywords: Alzheimer’s disease, Residual Neural Network (ResNet), Residual Neural Network (ResNet), Cluster Centroids, stratified sampling, ADASYN (Adaptive Synthetic Sampling)

DOI: 10.3233/JIFS-235059

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12095-12109, 2023