Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism

• Published in: SN computer science 2024-04, Vol.5 (4), p.392, Article 392
• Main Authors: Venkatesh, R., Anantharajan, Shenbagarajan, Gunasekaran, Shenbagalakshmi, Yogaraja, C. A., Gethzi Ahila Poornima, I.
• Format: Article
• Language: English
• Subjects: Advances in Computational Approaches for Image Processing; Alzheimer's disease; Biomarkers; Cloud Applications and Network Security; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Feature extraction; Filter banks; Gabor filters; Heterogeneity; Image classification; Image contrast; Image enhancement; Information Systems and Communication Service; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Original Research; Pattern Recognition and Graphics; Search algorithms; Software Engineering/Programming and Operating Systems; Swarm intelligence; Vision; Wireless Networks
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-02752-1

Alzheimer’s disease (AD) sufferers can benefit from early detection and diagnosis so that protective treatments can be taken before permanent brain impairment develops. Magnetic resonance imaging (MRI)-detected lesion sites in AD patients exhibit significant heterogeneity and these characteristics are spread throughout the imaging space, according to cross-sectional imaging studies of Alzheimer's disease. Because conventional Deep Learning (DL) methods can’t generate enough long-distance correlations in the characteristic space, adaptive fine-tuned deep resnet-50 with attention mechanism (AFDR-AM) is introduced in this research for the early identification of AD. The input MRI data is de-noised using a wavelet thresholding approach, and the contrast is enhanced using Improved Color Histogram Equalization (ICHE). Image recognition and classification characteristics are extracted at the feature extraction phase using a Gabor Filter Bank (GFB). To narrow down a big list of traits, the Artificial Bee Colony Optimization (ABCO) technique can be used to pick the most useful ones and get rid of the rest. Measures of accuracy, precision, recall, f1-score, specificity, and MSE are used to evaluate the effectiveness of the suggested strategy. Comprehensive experiments reveal that our approach achieved the best performance compared to other approaches.