Big Medical Image Analysis: Alzheimer’s Disease Classification Using Convolutional Autoencoder
Abstract: Deep learning-based analysis is a noticeable topic in recent years. The enormous success of deep learning is now combined with big data analytics to provide an open platform to the healthcare industry for a better diagnosis of any disease. In this paper, we described the convolutional autoencoder technique that reduces the complexity of radiologists through a brief study of Alzheimer's MRI data, which led to a rise in data-driven medical research for a better diagnosis. In this research, we have compared the effects of two techniques: convolutional autoencoder (CANN) and independent component analysis (ICA), and discovered that CANN has a higher accuracy of 99.42% and outperforms ICA models in terms of convergence speed.
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| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Instituto Politécnico Nacional, Centro de Investigación en Computación
2022
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462022000401491 |
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