CSCOOT: Competitive Swarm Coot Optimization-Based CNN Transfer Learning for Alzheimer's Disease Classification
Keywords:
Convolutional Neural Network (CNN), Deep Neuro Fuzzy Network (DNFN), Competitive Swarm Optimizer (CSO), COOT optimizer.Abstract
A person with Alzheimer's disease (AD) experiences a gradual decline in brain function and a total loss of cognitive capacity. Currently, it is said that differentiating between AD stages is a difficult task because many of these stages overlap. Hence, a recently suggested strategy for Alzheimer's disease classification using Competitive Swarm Coot Optimisation in a The CSCOOT_CNN Convolutional Neural Network does well when trained via transfer learning. Input photographs are sourced from the ADNI collection before moving on to the pre-processing stage. One of the pre-processing modules uses a median filter to get rid of noise. After that, feature extraction is performed on the pre-processed image, specifically for convolutional neural network (CNN) feature extraction. The next step is to feed the collected features into the classification level, where the disease is labelled using convolutional neural networks (CNNs) with transfer learning. In this case, the CNNs are trained using hyper parameters from the visual geometry group 19 (VGG19) model. Furthermore, the suggested CSCOOT algorithm is used to fine-tune CNNs that incorporate transfer learning. In addition, a new method called CSCOOT, which combines the COOT optimizer with Competitive Swarm Optimizer (CSO), has been introduced. Therefore, with values of 0.926 for accuracy, 0.979 for specificity, and 0.909 for sensitivity, the suggested CSCOOT_CNN achieved better results.
Downloads
References
Atif Mehmood, Muazzam Maqsood, Muzaffar Bashir and Yang Shuyuan, "A deep Siamese convolution neural network for multi-class classification of Alzheimer disease", Brain sciences, vol.10, no.2, pp.84, 2020.
Dr. Rachna Jaina, Nikita Jain, Akshay Aggarwal and D. Jude Hemanth, "Convolutional neural network based AD classification from magnetic resonance brain images", Cognitive Systems Research, vol.57, pp.147-159, 2019.
Farheen Ramzan, Muhammad Usman Ghani Khan, Asim Rehmat. Sajid Iqbal, Tanzila Saba, Amjad Rehman and Zahid Mehmood, "A deep learning approach for automated diagnosis and multi-class classification of AD stages using resting-state fMRI and residual neural networks", Journal of medical systems, vol.44, no.2, pp.1-16, 2020.
Sakeena Javaid, Muhammad Abdullah, Nadeem Javaid, Tanzeela Sultana, Jawad Ahmed and Norin Abdul Sattar, "Towards buildings energy management: Using seasonal schedules under time of use pricing tariff via deep neuro-fuzzy optimizer", In proceedings of 2019 15th international wireless communications & mobile computing conference (IWCMC), pp. 1594-1599, June 2019.
Suriya Murugan, Chandran Venkatesan, M. G. Sumithra, Xiao-Zhi Gao, B. Elakkiya, M. Akila and S. Manoharan, "DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images", IEEE Access, vol.9, pp.90319-90329, 2021.
Hadeer A. Helaly, Mahmoud Badawy and Amira Y. Haikal, "Deep learning approach for early detection of Alzheimer’s disease", Cognitive computation, pp.1-17, 2021.
Ravi Chandaran Suganthe, Rukmani Sevalaiappan Latha, Muthusamy Geetha and Gobichettipalayam Ramakrishnan Sreekanth, "Diagnosis of AD from brain magnetic resonance imaging images using deep learning algorithms", Advances in Electrical and Computer Engineering, vol.20, no.3, pp.57-64, 2020.
Ahsan Bin Tufail, Yong-Kui Ma and Qiu-Na Zhang, "Binary classification of AD using sMRI imaging modality and deep learning", Journal of digital imaging, vol.33, no.5, pp.1073-1090, 2020.
Susanne G. Muellera, Michael W. Weinera, Leon J. Thal, Ronald C. Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowski, Arthur W. Toga and Laurel Beckett, "Ways toward an early diagnosis in Alzheimer’s disease: the AD Neuroimaging Initiative (ADNI)", Alzheimer's & Dementia, vol.1, no.1, pp.55-66, 2005.
J. C. Baron, G. Chetelat, B. Desgranges, G. Perchey, B. Landeau,V. de la Sayette and F. Eustache, "In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease", Neuroimage, vol.14, no.2, pp.298-309, 2001.
Shishir Goswami, Anand Saoji, Navneet Kumar, Vijay Thawani, Meenal Tiwari and Manasi Thawani, "Effect of Bacopa monnieri on Cognitive functions in AD patients", International Journal of Collaborative Research on Internal Medicine & Public Health, vol.3, no.4, pp.285-293, 2011.
Hiroki Fuse, Kota Oishi, Norihide Maikusa, Tadanori Fukami and Japanese AD Neuroimaging Initiative, "Detection of AD with shape analysis of MRI images", In proceedings of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), pp. 1031-1034, December 2018.
Ewald Moser, Andreas Stadlbauer, Christian Windischberger, Harald H. Quick and Mark E. Ladd, "Magnetic resonance imaging methodology", European journal of nuclear medicine and molecular imaging, vol.36, no.1, pp.30-41, 2009.
R. Sampath and A. Saradha, "AD Image Segmentation with Self-Organizing Map Network", Journal of Software, vol.10, no.6, pp.670-680, June 2015.
Eman M. Ali, Ahmed F. Seddik and Mohamed H. Haggag, "Automatic detection and classification of AD from MRI using TANNN", International Journal of Computer Applications, vol.148, no.9, 2016.
Ahmad Waleed Salehi, Preety Baglat and Gaurav Gupta, "AD diagnosis using deep learning techniques", International Journal of Engineering and Advanced Technology (IJEAT), vol.9, no.3, pp.874-880, February 2020.
Saman Sarraf, Danielle D. DeSouza, John Anderson, Ghassem Tofighi, for the AD Neuroimaging Initiative, "DeepAD: AD classification via deep convolutional neural networks using MRI and fMRI", BioRxiv, pp.070441, 2017.
Ning Chun-yu, Liu Shu-fen and Qu Ming, "Research on removing noise in medical image based on median filter method", In proceedings of 2009 IEEE International Symposium on IT in Medicine & Education, vol.1, pp. 384-388, August 2009.
Ruizhe Yao, Ning Wang, Zhihui Liu, Peng Chen and Xianjun Sheng, "Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach", Sensors, vol.21, no.2, pp.626, 2021.
Muhammad Mateen, Junhao Wen, Nasrullah, Sun Song and Zhouping Huang, "Fundus image classification using VGG-19 architecture with PCA and SVD", Symmetry, vol.11, no.1, pp.1, 2018.
Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Muhammad A. Kadir and Saad Kashem, "Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray", Applied Sciences, vol.10, no.9, pp.3233, 2020.
Omolbanin Yazdanbakhsh and Scott Dick, "A deep neuro-fuzzy network for image classification", arXiv preprint arXiv:2001.01686, 2019.
Ran Cheng and Yaochu Jin, "A competitive swarm optimizer for large scale optimization", IEEE transactions on cybernetics, vol.45, no.2, pp.191-204, 2014.
Iraj Naruei and Farshid Keynia, "A new optimization method based on COOT bird natural life model", Expert Systems with Applications, vol.183, pp.115352, 2021.
Pawan R. Bhaladhare and Devesh C. Jinwala, "A clustering approach for the -diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm", Advances in Computer Engineering, vol.2014, 2014.
Avinash Gopal, "Hybrid classifier: Brain Tumor Classification and Segmentation using Genetic-based Grey Wolf optimization", Multimedia Research, vol 3, no 2, 2020.
G.Gokulkumari, "Classification of Brain tumor using Manta Ray Foraging Optimization-based DeepCNN classifier", Multimedia Research, vol 3, no 4, 2020.
Allada A.; Bhavani, R.; Chaduvula, K. & Priya R. (2023). Early Diagnosis of Alzheimer Disease from Mri Using Deep Learning Models. Journal of Information Technology Management, 15 (Special Issue), 52-71. https://doi.org/ 10.22059/jitm.2022.89411
Kumar, A., Dhabliya, D., Agarwal, P., Aneja, N., Dadheech, P., Jamal, S.S., Antwi, O.A. Cyber-Internet Security Framework to Conquer Energy-Related Attacks on the Internet of Things with Machine Learning Techniques (2022) Computational Intelligence and Neuroscience, 2022, art. no. 8803586,
Veeraiah, D., Mohanty, R., Kundu, S., Dhabliya, D., Tiwari, M., Jamal, S.S., Halifa, A. Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques (2022) Computational Intelligence and Neuroscience, 2022, art. no. 4003403,
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
