Kruthika KR, Maheshappa HD: Alzheimer’s disease neuroimaging initiative. Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Informatics in Medicine Unlocked 14:34-42, 2019

Kadri R, Tmar M, Bouaziz B: Alzheimer’s disease prediction using EfficientNet and Fastai. In Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part II 14:452–463, 2021. Springer International Publishing.

Karthick S, Muthukumaran N: Deep RegNet-150 architecture for single image super resolution of real-time unpaired image data. Applied Soft Computing. 162:111837, 2024. https://doi.org/10.1016/j.asoc.2024.111837

Ito K: Brain MRI image analysis technologies and its application to medical image analysis of Alzheimer’s diseases. Multidisciplinary Computational Anatomy: Toward Integration of Artificial Intelligence with MCA-based Medicine 157–62, 2022

Brand L, Nichols K, Wang H, Shen L, Huang H: Joint multi-modal longitudinal regression and classification for alzheimer’s disease prediction. IEEE transactions on medical imaging 39(6):1845–55, 2019

Andrushia AD, Sagayam KM, Dang H, Pomplun M, Quach L: Visual-saliency-based abnormality detection for MRI brain images—Alzheimer’s disease analysis. Applied Sciences 11(19):9199, 2021

Lanjewar MG, Parab JS, Shaikh AY: Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images. Multimedia Tools and Applications 82(8):12699-717, 2023

Karthick S, Muthukumaran N: Deep Regression Network for Single-Image Super-Resolution Based on Down- and Upsampling with RCA Blocks. National Academy Science Letters. 47:279–283, 2024. https://doi.org/10.1007/s40009-023-01353-5

Jang I, Li B, Riphagen JM, Dickerson BC, Salat DH: Alzheimer’s disease neuroimaging initiative. Multiscale structural mapping of Alzheimer’s disease neurodegeneration. NeuroImage: Clinical 33:102948, 2022

Menagadevi M, Mangai S, Madian N, Thiyagarajan D: Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine. Optik 272:170212, 2023

Murugan S, Venkatesan C, Sumithra MG, Gao XZ, Elakkiya B, Akila M, Manoharan S: DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. Ieee Access 9:90319-29, 2021

Kang W, Lin L, Zhang B, Shen X, Wu S: Alzheimer’s disease neuroimaging initiative. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis. Computers in Biology and Medicine 136:104678, 2021

Razzak I, Naz S, Alinejad-Rokny H, Nguyen TN, Khalifa F: A cascaded mutliresolution ensemble deep learning framework for large scale Alzheimer’s disease detection using brain MRIs. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2022.

Ismail WN, Rajeena PP F, Ali MA: MULTforAD: multimodal MRI neuroimaging for Alzheimer’s disease detection based on a 3D convolution model. Electronics 11(23):3893, 2022

Tuvshinjargal B, Hwang H: VGG-C transform model with batch normalization to predict Alzheimer’s disease through MRI dataset. Electronics 11(16):2601, 2022

Sharma, S., Gupta, S., Gupta, D., Altameem, A., Saudagar, A. K. J., Poonia, R. C., & Nayak, S. R. (2022). HTLML: hybrid AI based model for detection of Alzheimer’s disease. Diagnostics, 12(8), 1833

Rallabandi, V. S., Tulpule, K., Gattu, M., & Alzheimer's Disease Neuroimaging Initiative. (2020). Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer’s disease using structural MRI analysis. Informatics in Medicine Unlocked, 18, 100305

Sharma, S., Guleria, K., Tiwari, S., & Kumar, S. (2022). A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer disease using MRI scans. Measurement: Sensors, 24, 100506.

Khan, A., & Zubair, S. (2022). Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease. Journal of King Saud University-Computer and Information Sciences, 34(10), 8000-8018

Fareed, M. M. S., Zikria, S., Ahmed, G., Mahmood, S., Aslam, M., Jillani, S. F. ,..., & Asad, M. (2022). ADD-Net: an effective deep learning model for early detection of Alzheimer disease in MRI scans. IEEE Access, 10, 96930-96951.

Xiang, T., Zhang, C., Wang, X., Song, Y., Liu, D., Huang, H., & Cai, W. (2022). Towards bi-directional skip connections in encoder-decoder architectures and beyond. Medical Image Analysis, 78, 102420

Durgamahanthi, V., Anita Christaline, J., & Shirly Edward, A. (2021). GLCM and GLRLM based texture analysis: application to brain cancer diagnosis using histopathology images. In Intelligent Computing and Applications: Proceedings of ICICA 2019 (pp. 691-706). Springer Singapore.

Muthukrishnan A, Kumar DV, Kanagaraj M: Internet of image things-discrete wavelet transform and Gabor wavelet transform based image enhancement resolution technique for IoT satellite applications. Cognitive Systems Research 57:46-53, 2019

Wajid SK, Hussain A, Huang K, Boulila W: Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques. In2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) 359-366, 2016. IEEE.

Dhiman G, Kumar V: Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowledge-based systems 165:169-96, 2019

RM SP, Maddikunta PK, Parimala M, Koppu S, Gadekallu TR, Chowdhary CL, Alazab M: An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Computer Communications 160:139-49, 2020

Trojovský P, Dehghani M: Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22(3):855, 2022

SARVESH DUBEY, (2019) kaggle [https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images]. Accessed on 27-10-2022