Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis
Conference paper
Sami, N., Makkar, A., Meziane, F. and Conway, M. 2024. Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis. International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer. https://doi.org/10.1007/978-3-031-53085-2_17
| Authors | Sami, N., Makkar, A., Meziane, F. and Conway, M. |
|---|---|
| Type | Conference paper |
| Abstract | Alzheimer’s disease (AD) is a debilitating, irreversible neurological condition that leads to a severe decline in patients’ health, often resulting in fatality. Detecting AD and its early stages, such as mild cognitive impairment (MCI), which can manifest as stable (sMCI) or progressing (pMCI), is crucial for effective treatment planning and personalized therapy. Recent advancements in noninvasive retinal imaging technologies, including Optical Coherence Tomography (OCT), OCT angiography, and digital retinal photography, have enabled the examination of the neuronal and vascular structure of the retina in AD patients. Furthermore, the development of computer algorithms tailored to these imaging techniques has significantly enhanced AD research. This paper presents a comprehensive study on early AD identification that leverages state-of-the-art deep learning techniques and medical images or scans. It also explains the potential benefits of using emerging retinal scans for enhanced detection. It also explains various deep learning techniques that harness both local and global features to enhance accuracy by utilizing extensive scan data. |
| Keywords | Alzheimer’s disease (AD); Retinal imaging ; Deep learning |
| Year | 2024 |
| Conference | International Conference on Recent Trends in Image Processing and Pattern Recognition |
| Publisher | Springer |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-53085-2_17 |
| Web address (URL) | https://link.springer.com/chapter/10.1007/978-3-031-53085-2_17 |
| ISBN | 978-3-031-53085-2 |
| Output status | Published |
| Publication dates | |
| Online | 31 Jan 2024 |
| Publication process dates | |
| Deposited | 07 Mar 2024 |
Permalink -
https://repository.derby.ac.uk/item/q512w/exploring-imaging-biomarkers-for-early-detection-of-alzheimer-s-disease-using-deep-learning-a-comprehensive-analysis
65
total views0
total downloads11
views this month0
downloads this month
Export as
Related outputs
An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management
Gupta, K., Saxena, D., Rani, P., Kumar, J., Makkar, A. and Singh, A. K. 2024. An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management. IEEE Transactions on Automation Science and Engineering. pp. 1-12. https://doi.org/10.1109/TASE.2024.3456209A Global Cybersecurity Standardization Framework for Healthcare Informatics
Gupta, K., Mishra, V. and Makkar, A. 2024. A Global Cybersecurity Standardization Framework for Healthcare Informatics. IEEE Journal of Biomedical and Health Informatics. pp. 1-8. https://doi.org/10.1109/JBHI.2024.3467179Enhancing robotics learning using imitation learning through visual-based behaviour cloning
Jadeja, Y., Shafik, M., Wood, P. and Makkar, A. 2024. Enhancing robotics learning using imitation learning through visual-based behaviour cloning. 21st International Conference on Manufacturing Research. EDP Sciences. https://doi.org/10.1051/matecconf/202440112006Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification
Abdi, A., Melhem, W. and Meziane, F. 2024. Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification. Applied Sciences. 14 (23), pp. 1-21. https://doi.org/10.3390/app142311009Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs
Melhem, W., Abdi, A. and Meziane, F. 2024. Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs. 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0013066900003838Unequal-Radius Clustering in WSN-Based IoT Networks : Energy Optimization and Load Balancing in UDCOPA Protocol
Mir, F. and Meziane, F. 2024. Unequal-Radius Clustering in WSN-Based IoT Networks : Energy Optimization and Load Balancing in UDCOPA Protocol. The Journal of Supercomputing. pp. 1-32. https://doi.org/10.1007/s11227-024-06426-wDiferentiation of SH‑SY5Y neuroblastoma cells using retinoic acid and BDNF: a model for neuronal and synaptic diferentiation in neurodegeneration
Taggart, I., Crompton, L. A., Conway, M. and Craig, T. J. 2024. Diferentiation of SH‑SY5Y neuroblastoma cells using retinoic acid and BDNF: a model for neuronal and synaptic diferentiation in neurodegeneration. In Vitro Cellular and Developmental Biology - Animal. https://doi.org/10.1007/s11626-024-00948-6Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol
Mir, F., Meziane, F., Bounceur, A. and Laouid, A. 2024. Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol. 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE Computer Society. https://doi.org/10.1109/PAIS62114.2024.10541142EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria for Improved Clustering
Mir, F., Bounceur, A. and Meziane, F. 2024. EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria for Improved Clustering. Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS '23). ACM Press. https://doi.org/10.1145/3644713.36448Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function
Hasan, A.M., Khalaf, M., Sabbar, B.M., Ibrahim, R.W., A. Jalab, H.A. and Meziane, F. 2024. Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function. Baghdad Science Journal. 21 (12), pp. 1-11. https://doi.org/10.21123/bsj.2024.9286LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model
Al Qerom, M., Otair, M., Meziane, F., AbdulRahman, S. and Alzubi, M. 2024. LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model. Journal of Robotics and Control. 5 (5), pp. 1311-3121. https://doi.org/10.18196/jrc.v5i5.21834A graph based named entity disambiguation using clique partitioning and semantic relatedness
Belalta, R., Belazzoug, M. and Meziane, F. 2024. A graph based named entity disambiguation using clique partitioning and semantic relatedness. Data and Knowledge Engineering. pp. 1-27. https://doi.org/10.1016/j.datak.2024.102308Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657Preserving Accuracy in Federated Learning via Equitable Model and Efficient Aggregation
Mehdi, M., Makkar, A., Conway, M. and Sama. L 2024. Preserving Accuracy in Federated Learning via Equitable Model and Efficient Aggregation. International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer Nature. https://doi.org/10.1007/978-3-031-53082-1_7Effects of carotenoids on mitochondrial dysfunction
Ademowo, S., Oyebode, O., Edward, R., Conway, M., Griffiths, H. and Dias, I. H. K. 2024. Effects of carotenoids on mitochondrial dysfunction. Biochemical Society Transactions. 52 (1), p. 65–74. https://doi.org/10.1042/BST20230193Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks
Mir, F. and Meziane, F. 2024. Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks. Expert Systems with Applications. 247, pp. 1-23. https://doi.org/10.1016/j.eswa.2024.123212Molecular subtypes classification of breast cancer in DCE-MRI using deep features
Hasan, A.M., Al-Waely, N.K.N., Aljobouri, H.K., Jalab, H.A., Ibrahim, R.W. and Meziane, F. 2024. Molecular subtypes classification of breast cancer in DCE-MRI using deep features. Expert Systems with Applications. 236, pp. 1-8. https://doi.org/10.1016/j.eswa.2023.121371Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies
Gaba, S., Budhiraja, S., Kumar, V. and Makkar, A. Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies. Communications in Analysis and Mechanics. 21 (1), pp. 1527-155. https://doi.org/10.3934/mbe.2024066SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X‑rays
Makkar, A. and Santosh, K. C. 2023. SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X‑rays. International Journal of Machine Learning and Cybernetics. 14, p. 2659–2670. https://doi.org/10.1007/s13042-023-01789-7Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging
Hasan, A.M., Aljobouri, H.K., Al-Waely, K.N.A., Ibrahim, W.I., Jalab, H.A. and Meziane, F. 2023. Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging. Neural Computing and Applications. pp. 1-14. https://doi.org/10.1007/s00521-023-08909-yUnequal Clustering Protocol in IoT Networks Based on Multiple Criteria Processing
Mir, F. and Meziane, F. 2023. Unequal Clustering Protocol in IoT Networks Based on Multiple Criteria Processing. 6th International Conference on Information Science and Systems (ICISS 2023). ACM Press.A Fuzzy-based approach to Enhance Cyber Defence Security for Next-generation IoT
Makkar, A., Ghosh, U., Sharma, P.K. and Javed, A. 2023. A Fuzzy-based approach to Enhance Cyber Defence Security for Next-generation IoT. IEEE Internet of Things Journal. Vol 10 (Issue 3), pp. 2079-2086. https://doi.org/10.1109/jiot.2021.3053326BCAT1 redox function maintains mitotic fidelity
Francois, L., Boskovic, P., Knerr, J., He, W., Sigismondo, G., Schwan, C., More, T. H., Schlotter, M., Conway, M., Krijgsveld, J., Hiller, K. and Grosse, R. 2023. BCAT1 redox function maintains mitotic fidelity. Cell Reports. 42 (3), pp. 1-25.Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion
Hasan, A.M., Al-Waely, N.K.N., Ajobouri, H.K., Ibrahim, R.W., Jalab, H.A. and Meziane, F. 2023. Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion. Biomedical Signal Processing and Control. 84, pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105002The Impact of Arabic Diacritization on Word Embeddings
Abbache, M., Abbache, A., Xu, J.W., Meziane, F. and Wen, X.B. 2023. The Impact of Arabic Diacritization on Word Embeddings. ACM Transactions on Asian and Low-Resource Language Information Processing . pp. 1-32. https://doi.org/10.1145/3592603
A review of the generation of requirements specification in natural language using objects UML models and domain ontology
Abdalazeima, Alaa and Meziane, Farid 2021. A review of the generation of requirements specification in natural language using objects UML models and domain ontology. Procedia Computer Science. 189, pp. 328-334. https://doi.org/10.1016/j.procs.2021.05.102Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications
Gupta, R., Saxena. R., Gupta, I., Makkar, A. and Sing, A. K. 2022. Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications. IEEE Networking Letters . 4 (4), pp. 174-178. https://doi.org/https://doi.org/10.1109/LNET.2022.3200724SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city
Aaisha Makkar 2022. SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city. Sustainable Cities and Society. 79 (April 2022), p. 103658. https://doi.org/10.1016/j.scs.2021.103658SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach
Aaisha Makkar, Tae Woo Kim, Ashutosh Kumar Singh, Jungho Kang and Jong Hyuk Park 2022. SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach. IEEE Transactions on Industrial Informatics. 18 (9), pp. 6406 - 6414. https://doi.org/10.1109/tii.2022.3149902The BCAT1 CXXC Motif Provides Protection against ROS in Acute Myeloid Leukaemia Cells
Hillier, J., Allcott, G. J., Guest, A. L., Heaselgrave, W., Tonks, A., Conway, M., Cherry, A. L. and Coles, S. J. 2022. The BCAT1 CXXC Motif Provides Protection against ROS in Acute Myeloid Leukaemia Cells. Antioxidants. 11 (4), pp. 1-23. https://doi.org/10.3390/antiox11040683Mitigation of Popularity Bias in Recommendation Systems
Karboua, S., Harrag, F., Meziane, F. and Boutadjine, A. 2022. Mitigation of Popularity Bias in Recommendation Systems. Tunisian-Algerian Joint Conference on Applied Computing. Constantine, Algeria 14 - 15 Dec 2022Describing Pulmonary Nodules Using 3D Clustering
Al-Funjan, A., Farid Meziane and Aspin, R. 2022. Describing Pulmonary Nodules Using 3D Clustering. Advanced Engineering Research. 22 (3), pp. 261-271. https://doi.org/10.23947/2687-1653-2022-22-3-261-271Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. https://doi.org/10.33736/jcsi.4761.2022DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Mir, F. and Meziane, F. 2022. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Computing. 26, p. 1077–1098. https://doi.org/10.1007/s10586-022-03741-wBotnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305Visualization and deep-learning-based malware variant detection using OpCode-level features
Darem, A., Abawajy, J., Makkar, A., Alhashmi, A. and Alanazi, S. 2021. Visualization and deep-learning-based malware variant detection using OpCode-level features. Future Generation Computer Systems. Vol 125 (Dec 2021), pp. 314-323. https://doi.org/10.1016/j.future.2021.06.032An Efficient Spam Detection Technique for IoT Devices Using Machine Learning
Makkar, A., Garg, S., Kumar, N., Hossain, M.S., Ghoneim, A. and Alrashoud, M. 2021. An Efficient Spam Detection Technique for IoT Devices Using Machine Learning. IEEE Transactions on Industrial Informatics. Vol 17 (Issue 2), pp. 903-912. https://doi.org/10.1109/tii.2020.2968927PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention
Makkar, A. and Kumar, N. 2021. PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention. Future Generation Computer Systems. 125 (Dec 2021), pp. 41-58. https://doi.org/10.1016/j.future.2021.06.026Artificial Intelligence and Edge Computing-enabled Web Spam Detection for Next Generation IoT Applications
Makkar, A., Ghosh, U. and Sharma, P.K. 2021. Artificial Intelligence and Edge Computing-enabled Web Spam Detection for Next Generation IoT Applications. IEEE Sensors Journal. 21 (Issue: 22), pp. 25352 - 25361. https://doi.org/10.1109/jsen.2021.3066492FedLearnSP: Preserving Privacy and Security using Federated Learning and Edge Computing
Makkar, A., Ghosh, U., Rawat, D.B. and Abawajy, J. 2021. FedLearnSP: Preserving Privacy and Security using Federated Learning and Edge Computing. IEEE. https://doi.org/10.1109/mce.2020.3048926Ai based management of food wastage
Sama, L., Makkar, A., Prokshitha, P., Sharma, B.K. and Dhaloria, D. 2021. Ai based management of food wastage. 2021 International Semantic Intelligence Conference, ISIC 2021; New Delhi; India; 25 February 2021 through 27 February 2021; Code 167510. CEUR Workshops Proceedings.An Intelligent Phishing Detection Scheme Using Machine Learning
Makkar, A., Kumar, N., Sama, L., Mishra, S. and Samdani, Y. Giri D., Buyya R., Ponnusamy S., De D., Adamatzky A. and Abawajy J.H. (ed.) An Intelligent Phishing Detection Scheme Using Machine Learning. Springer.MRI brain classification using the quantum entropy LBP and deep-learning-based features
Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. 2020. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 22 (9), p. 1033. https://doi.org/10.3390/e22091033