Multi-classification on the diagnosis of four early stages of Alzheimer’s disease by transfer learning models

Longling Geng

doi:10.1117/12.2671412

28 April 2023 Multi-classification on the diagnosis of four early stages of Alzheimer’s disease by transfer learning models

Longling Geng

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Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105O (2023) https://doi.org/10.1117/12.2671412
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Nowadays, deep learning models are widely used in medical diagnosis, with advantages of higher accuracy and better capability of analyzing complex medical images over traditional check. This work compares seven commonly used CNN models - VGG16, ResNet50, InceptionV3, DenseNet-121, Xception, Inception-Resnet V2, and MobilenetV3-small - on the diagnosis of the four early stages of Alzheimer’s Disease – Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Models are initially adjusted with a dense layer of four outputs on the multi-classification problem and deployed in various overfitting prevention setting. Also, by using transfer learning and fine-tuning technique, the training accuracy and testing accuracy of the seven models have both improved by over 30%. Among all, DenseNet121 has the overall best performance of 95.48% testing accuracy, 0.92 F1- score and second smallest number of parameters of 7,041,604, with a good portability on mobile computing devices, likely due to the reuse of feature maps to extract more accurate features. Inception-ResNet-v2 is adopted when only considering accuracy and reliability and due to its largest number of parameters, Inception-ResNet-v2 is more suggested to be used on computer medical application. VGG16 has the highest accuracy of 96.11% and a small number of parameters of 14,716,740, however, it is currently not suitable for applying to real-life application since its F1-score of 0.8943 and precision of 0.8266, indicating numerous false cases.

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Longling Geng "Multi-classification on the diagnosis of four early stages of Alzheimer’s disease by transfer learning models", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105O (28 April 2023); https://doi.org/10.1117/12.2671412

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KEYWORDS

Education and training

Alzheimer disease

Data modeling

Performance modeling

Magnetic resonance imaging

Overfitting

Mobile devices

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