Vol.:(0123456789) Multimedia Tools and Applications https://doi.org/10.1007/s11042-023-17867-5 1 3 BiLSTM-ANN: early diagnosis of Alzheimer’s disease using hybrid deep learning algorithms Princy Matlani 1 Received: 1 November 2022 / Revised: 4 October 2023 / Accepted: 13 December 2023 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disease that afects cognition, behavior, and memory, eventually reaching a point where daily activities are impaired. Although there is currently no cure, initiating a well-considered management approach in the early stages can improve quality of life and potentially slow disease progression. Vari- ous machine learning (ML) techniques are widely used in clinical research to aid in the detection and tracking of disease states. Magnetic resonance imaging (MRI) is considered one of the most efective tools for diagnosing Alzheimer’s disease. However, detecting sub- tle changes in the AD-afected brain in the early stages presents a signifcant challenge. The main challenges are the extremely small numbers of trained samples and larger feature descriptions. Hence, in this research, automatic AD can be diagnosed through the adoption of hybrid deep learning (DL) methodologies. For image pre-processing, Improved adaptive wiener fltering (IAWF) is utilized to enhance the acquired images. Then, the features are extracted by an efective hybrid method named Principal Component Analysis, which uses a Normalized Global Image Descriptor (PCA-NGIST) to extract the signifcant features from images without any image segmentation. Next, the best features are selected using the Improved Wild Horse Optimization algorithm (IWHO). Finally, the disease is diag- nosed by hybrid Bi-directional Long Short-Term Memory with Artifcial Neural Network (BiLSTM-ANN). The suggested method is implemented on the MATLAB platform. An accuracy of 99.22% is attained for the ADNI dataset and 98.96% for the OASIS dataset, which are comparatively better than the state-of-the-art methods. Keywords Degenerative neurological disorder · Alzheimer’s disease · Adaptive wiener fltering · Normalized global image descriptor · Wild horse optimization * Princy Matlani princy.matlani@gmail.com 1 Computer Science & Engineeering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India