Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation
| Autor: | Suliman Mohamed Fati, Muhammad Sajjad, Farheen Ramzan, Amjad Rehman, Saeed Ali Bahaj, Muhammad Usman Ghani Khan, Mahyar Kolivand |
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| Rok vydání: | 2021 |
| Předmět: |
Histology
Computer science Synthetic data Alzheimer Disease Brain positron emission tomography medicine Image Processing Computer-Assisted Humans Cognitive impairment Instrumentation medicine.diagnostic_test business.industry Deep learning Disease classification Brain Pattern recognition medicine.disease Medical Laboratory Technology Positron emission tomography Positron-Emission Tomography Artificial intelligence Neural Networks Computer Anatomy Alzheimer's disease business Generative adversarial network |
| Zdroj: | Microscopy research and techniqueREFERENCES. 84(12) |
| ISSN: | 1097-0029 |
| Popis: | With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI. |
| Databáze: | OpenAIRE |
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