Performance Evaluation of Various Optimizers on Alzheimer’s Disease Classification Using Deep Neural Network
DOI:
https://doi.org/10.6977/IJoSI.202412_8(4).0002Keywords:
Alexne, Googlenet, Squeezenet, Mobilenetv2, ADAM, RMSProp, SGDM, AD ClassificationAbstract
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 computer vision tasks and have proven to be effective in image analysis. Additionally, three different optimizers are evaluated: 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 classification accuracy of 91% among all the tested classifiers. 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 improved patient outcomes.
Downloads
Published
Issue
Section
License
Copyright in a work is a bundle of rights. IJoSI's, copyright covers what may be done with the work in terms of making copies, making derivative works, abstracting parts of it for citation or quotation elsewhere and so on. IJoSI requires authors to sign over rights when their article is ready for publication so that the publisher from then on owns the work. Until that point, all rights belong to the creator(s) of the work. The format of IJoSI copy right form can be found at the IJoSI web site.The issues of International Journal of Systematic Innovation (IJoSI) are published in electronic format and in print. Our website, journal papers, and manuscripts etc. are stored on one server. Readers can have free online access to our journal papers. Authors transfer copyright to the publisher as part of a journal publishing agreement, but have the right to:
1. Share their article for personal use, internal institutional use and scholarly sharing purposes, with a DOI link to the version of record on our server.
2. Retain patent, trademark and other intellectual property rights (including research data).
3. Proper attribution and credit for the published work.