Harnessing transfer learning for Alzheimer's disease prediction

Yukun Liu; Chengxuan Zheng; Baha Ihnaini

doi:10.1117/12.2667247

1 March 2023 Harnessing transfer learning for Alzheimer's disease prediction

Yukun Liu, Chengxuan Zheng, Baha Ihnaini

Proceedings Volume 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022); 125880Y (2023) https://doi.org/10.1117/12.2667247
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 2022, Chongqing, China

Abstract

Nowadays, Alzheimer's Disease (AD) has become a massive problem for middle-aged and older adults. Although due to its long incubation period and early mild symptoms, patients have a more extended period and more possibilities to check out, it is still hard for patients and doctors to diagnose in early routine examinations. This article provides a new method to help the doctor to diagnose Alzheimer's Disease in the early phase. We use transfer learning in deep learning to help diagnose Alzheimer's Disease early in developing Computed Tomography (CT) brain images. Using three pre-trained models, ShuffleNet, DenseNet, and NASNet-mobile as the transfer learning training model and convolution neural networks. We made some improvements to make it more relevant to the actual situation. DenseNet has best performance (87.36%) among the three models. We set the output into four classes: the four stages of Alzheimer's are widely recognized (Mild Demented, Moderate Demented, Very Mild Demented).

Citation Download Citation

Yukun Liu, Chengxuan Zheng, and Baha Ihnaini "Harnessing transfer learning for Alzheimer's disease prediction", Proc. SPIE 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 125880Y (1 March 2023); https://doi.org/10.1117/12.2667247

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KEYWORDS

Alzheimer's disease

Data modeling

Networks

Convolution

Education and training

Medical imaging

Diagnostics

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