Performance evaluation of various optimizers on Alzheimer’s dis-ease classification using deep neural network
The proposed work focuses on using transfer learning and CNN models for the classification of Alzheimer's Disease (AD) based on different classes of datasets. The goal is to improve the early diagnosis and classification of AD, which can contribute to better patient recovery and management. The study compares the performance of four different CNN models: AlexNet, GoogLeNet, SqueezeNet, and MobileNet V2. These models have been widely used in various com-puter vision tasks and have proven to be effective in image analysis. Additionally, three different optimizers are eval-uated: Stochastic Gradient Descent with Momentum (SGDM), RMSProp, and ADAM. Optimizers play a crucial role in training deep neural networks, as they determine how the model updates its weights during the learning process. According to the results of the study, the MobileNet V2 model with the SGDM optimizer achieved the highest classi-fication accuracy of 91% among all the tested classifiers. Here, datasets are taken from Kaggle and Mobilenet classi-fies the output into four classes namely Very Mild Demented, Mild Demented, Moderately Demented, Non-Demented. This suggests that this combination is particularly effective for AD diagnosis and classification based on the given datasets. The automated Alzheimer's disease classification system developed in this work has the potential to identify early signs and symptoms of the disease. Early detection is crucial because it allows medical professionals to intervene at an earlier stage, providing timely treatment and management strategies. By leveraging medical image analysis and transfer learning techniques, this system can contribute to more effective and efficient AD diagnosis, leading to im-proved patient outcomes.
- Ebrahimi-Ghahnavieh,A.,Luo, S., & Chiong, R.(2019), Transfer Learning for Alzheimer’s Dis-ease Detection on MRI Images, The 2019 IEEE International Conference on Industry 4.0, Arti-ficial Intelligence, and Communications Tech-nology (IAICT), IEEE 2019.
- Doaa Ahmed Arafa, Hossam El‑Din Moustafa, Hesham A. Ali,Amr M. T. Ali‑Eldin, & Sabry F. Saraya. (2023).A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images, Multimedia Tools andApplica-tions,2023, https://doi.org/10.1007/s11042-023-15738-7
- Antony, F., Anita, H.B., George, J.A. (2023).Classification on Alzheimer’s DiseaseMRI Images with VGG-16 and VGG-19. vol 312. Springer,
- Halebeedu Subbaraya Suresha, Srirangapatna Sampathkumaran Parthasarathy. (2020). Alzheimer Disease Detection Based on Deep Neural Network with Rectified Adam Optimization Technique Using MRI Analysis. *2020 IEEE*, 978-1-7281-9183-6/20/$31.00 © 2020.
- Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo, Jorge Samper-González, Alexandre Routier, Simona Bottani, Didier Dormont, Stanley Durrleman, Ninon Burgos, Olivier Colliot. (2020). Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation. *Medical Image Analysis*, 63, 101694.
- Mggdadi, E., Al-Aiad, A., Al-Ayyad, M. S., & Darabseh, A. (2021). Prediction of Alzheimer’s Disease from MRI Images Using Deep Learning. *2021 12th International Conference on Information and Communication Systems (ICICS)*, 120–125.
- Raza, N., Naseer, A., Tamoor, M., & Zafar, K. (2023). Alzheimer Disease Classification through Transfer Learning Approach. *Diagnostics*, 13, 801. https://doi.org/10.3390/diagnostics13040801
- Rallabandi, P. S., Tulpule, K., & Gattu, M. (2020). Automatic Classification of Cognitively Normal, Mild Cognitive Impairment, and Alzheimer’s Disease Using Structural MRI Analysis. *Informatics in Medicine Unlocked*, 18, 100305. https://doi.org/10.1016/j.imu.2020.100305
- Zaheer, R., & Shaziya, H. (2019). A Study of the Optimization Algorithms in Deep Learning. *International Conference on Inventive Systems and Control (ICISC 2019)*. IEEE. ISBN: 978-1-5386-3950-4. https://www.kaggle.com/datasets/tour-ist55/alzheimers-dataset-4-class-of-images
- Haji, S. H., & Abdulazeez, A. M. (2021). Comparison of Optimization Techniques Based on Gradient Descent Algorithm: A Review. *PJAEE*, 18(4).
- Pallawi, S., & Singh, D. K. (2023). Review and Analysis of Deep Neural Network Models for Alzheimer's Disease Classification Using Brain Medical Resonance Imaging. *Cognitive Computation and Systems*, 5(1), March 2023.
- Rallabandi, S., Tulpule, K., & Gattu, M. (2020). Automatic Classification of Cognitively Normal, Mild Cognitive Impairment and Alzheimer’s Disease Using Structural MRI Analysis. *Informatics in Medicine Unlocked*, 18, 100305.
- Zhang, V. P. S. J., Liu, M., An, L., Gao, Y., & Shen, D. (2016). Alzheimer’s Disease Diagnosis Using Landmark-Based Features from Longitudinal Structural MR Images. *IEEE*, 2168-2194.
- Fu’adah, Y. N., Wijayanto, I., Pratiwi, N. K. C., Taliningsih, F. F., Rizal, S., & Pramudito, M. A. (2020). Automated Classification of Alzheimer’s Disease Based on MRI Image Processing Using Convolutional Neural Network (CNN) with AlexNet Architecture. *Journal of Physics: Conference Series*, IOP Publishing, 1844(2021), 012020.
- Helaly, H. A., Badawy, M., & Haikal, A. Y. (2022). Deep Learning Approach for Early Detection of Alzheimer’s Disease. *Cognitive Computation*, 14: 1711–1727.
