期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 148, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.105944
关键词
Alzheimer's disease; Pyramid squeeze attention mechanism; Image filtering; Fully convolutional network; Image classification
类别
资金
- National Natural Science Foundation of China [62011530130, 61672466, 62101497]
- Joint Fund of Zhejiang Provincial Natural Science Foundation [LSZ19F010001]
- Key Research and Development Program of Zhejiang Province [2020C03060]
- 521 Talents project of Zhejiang Sci- Tech University
- ERC IMI [101005122]
- MRC [MC/PC/21013]
- Royal Society [IEC\ NSFC\211235]
- UKRI Future Leaders Fellowship [MR/V023799/1]
- H2020 [952172]
This study investigates the impact of different image filtering approaches and attention mechanisms on the image classification of Alzheimer's disease, and finds that these methods provide effective assistance for diagnosis and classification.
Brain medical imaging and deep learning are important foundations for diagnosing and predicting Alzheimer's disease. In this study, we explored the impact of different image filtering approaches and Pyramid Squeeze Attention (PSA) mechanism on the image classification of Alzheimer's disease. First, during the image pre-processing, we register MRI images and remove skulls, then apply median filtering, Gaussian blur filtering, and anisotropic diffusion filtering to obtain different experimental images. After that, we add the Squeeze and Excitation (SE) mechanism and Pyramid Squeeze Attention (PSA) mechanism to the Fully Convolutional Network (FCN) model respectively, to obtain each MRI image's corresponding feature information of disease probability map. Besides, we also construct Multi-Layer Perceptron (MLP) model's framework, combining feature information of disease probability map with age, gender, and Mini-Mental State Examination (MMSE) of each sample, to get the final classification performance of model. Among them, the accuracy of the MLP-C model combining anisotropic diffusion filtering with the Pyramid Squeeze Attention mechanism can reach 98.85%. The corresponding quantitative experimental results show that different image filtering approaches and attention mechanisms provide effective assistance for the diagnosis and classification of Alzheimer's disease.
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