AUTOMATIC DETECTION OF ALZHEIMER'S DISEASE BASED ON ARTIFICIAL INTELLIGENCE
Achraf Benba
achraf.benba@um5s.net.maMohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
http://orcid.org/0000-0001-7939-0790
Abdelilah Kerchaoui
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
https://orcid.org/0009-0004-9561-5960
Abstract
Alzheimer's disease is a neurodegenerative disease that progressively destroys neurons through the formation of platelets that prevent communication between neurons. The study carried out in this project aims to find a precise and relevant diagnostic solution based on artificial intelligence and which helps in the early detection of Alzheimer's disease in order to stop its progression. The study went through a process of processing MRI images followed by training of three deep learning algorithms (VGG-19, Xception and DenseNet121) and finally by a step of testing and predicting the results. The results of the accuracy metric obtained for the three algorithms were respectively 98%, 95%, 91%.
Keywords:
Alzheimer’s disorder, artificial intelligence, deep learning, signal processingReferences
Albawi S. et al.: Understanding of a convolutional neural network. International conference on engineering and technology (ICET), IEEE, 2017, 1–6.
DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186
Google Scholar
Brownlee J.: Gentle introduction to the Adam optimization algorithm for deep learning. Machine Learning Mastery 3, 2017.
Google Scholar
Chollet F.: Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 1251–1258.
DOI: https://doi.org/10.1109/CVPR.2017.195
Google Scholar
Jain R. et al.: Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research 57, 2019, 147–159.
DOI: https://doi.org/10.1016/j.cogsys.2018.12.015
Google Scholar
Kaplan E. et al.: Feed-forward LPQNet based automatic alzheimer's disease detection model. Computers in Biology and Medicine 137, 2021, 104828.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104828
Google Scholar
Liu M. et al.: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208, 2020, 116459.
DOI: https://doi.org/10.1016/j.neuroimage.2019.116459
Google Scholar
Shanmugam J. V. et al.: Alzheimer’s disease classification using pre-trained deep networks. Biomedical Signal Processing and Control 71, 2022, 103217.
DOI: https://doi.org/10.1016/j.bspc.2021.103217
Google Scholar
Szegedy C. et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI conference on artificial intelligence, 2017.
DOI: https://doi.org/10.1609/aaai.v31i1.11231
Google Scholar
World Health Organization, 2012. Dementia: a public health priority. World Health Organization.
Google Scholar
Yang K. et al.: Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy. Proceedings of the 2020 conference on fairness, accountability, and transparency, 547–558.
DOI: https://doi.org/10.1145/3351095.3375709
Google Scholar
Authors
Achraf Benbaachraf.benba@um5s.net.ma
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
http://orcid.org/0000-0001-7939-0790
Authors
Abdelilah KerchaouiMohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
https://orcid.org/0009-0004-9561-5960
Statistics
Abstract views: 215PDF downloads: 261
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Achraf Benba, Fatima Zahra El Attaoui, Sara Sandabad, IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED ECG ACQUISITION SYSTEM FOR THE DETECTION OF CARDIAC ABNORMALITIES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 1 (2023)
- Oumaima Majdoubi, Achraf Benba, Ahmed Hammouch, CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 3 (2023)
- Oumaima Majdoubi, Achraf Benba, Ahmed Hammouch, COMPREHENSIVE MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR PARKINSON'S DISEASE CLASSIFICATION AND SEVERITY ASSESSMENT , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 4 (2023)
- Achraf Benba, Mouna Akki, Sara Sandabad, THE APPLICATION OF MACHINE LEARNING ON THE SENSORS OF SMARTPHONES TO DETECT FALLS IN REAL-TIME , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 2 (2023)
