{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:14Z","timestamp":1777705034447,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Early Alzheimer\u2019s 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\u2019s 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\u2019s and non-Alzheimer\u2019s 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\u2019s 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\u2019s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer\u2019s 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.<\/jats:p>","DOI":"10.3233\/jifs-235059","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T10:51:00Z","timestamp":1697799060000},"page":"12095-12109","source":"Crossref","is-referenced-by-count":12,"title":["Alzheimer\u2019s disease detection using residual neural network with LSTM hybrid deep learning models"],"prefix":"10.1177","volume":"45","author":[{"given":"R.","family":"Vidhya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhanalaxmi","family":"Banavath","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Kayalvili","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Kongu Engineering College, Perundurai, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swarna Mahesh","family":"Naidu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Charles Prabu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Sugumar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Hemalatha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, St. Joseph\u2019s College of Engineering, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Vimal","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.G.","family":"Vidhya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, HKBK College of Engineering, Karnataka, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-235059_ref1","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1049\/iet-ipr.2019.0617","article-title":"Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer\u2019s disease diagnosis","volume":"14","author":"Fang","year":"2020","journal-title":"IET Image Processing"},{"issue":"2","key":"10.3233\/JIFS-235059_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-018-0080-3","article-title":"Brain MRI analysis for Alzheimer\u2019s disease diagnosis using an ensemble system of deep convolutional neural networks","volume":"5","author":"Islam","year":"2018","journal-title":"Brain Inform"},{"key":"10.3233\/JIFS-235059_ref5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compbiomed.2017.02.011","article-title":"Classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment-to-Alzheimer\u2019s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm","volume":"83","author":"Beheshti","year":"2017","journal-title":"Comput Biol Med"},{"key":"10.3233\/JIFS-235059_ref6","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.conb.2020.02.003","article-title":"Amyloid beta-protein and beyond: The path forward in Alzheimer\u2019s disease","volume":"61","author":"Walsh","year":"2020","journal-title":"Curr Opin Neurobiol"},{"key":"10.3233\/JIFS-235059_ref7","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1111\/jnc.15023","article-title":"What works and what does not work in Alzheimer\u2019s disease? 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