Resumo

MANSINGH, Padmini; PATTANAYAK, Binod Kumar  e  PATI, Bibudhendu. Big Medical Image Analysis: Alzheimer’s Disease Classification Using Convolutional Autoencoder. Comp. y Sist. [online]. 2022, vol.26, n.4, pp.1491-1501.  Epub 17-Mar-2023. ISSN 2007-9737.  https://doi.org/10.13053/cys-26-4-4090.

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.

Palavras-chave : Deep learning; big data analytics; CANN; ICA; healthcare; machine learning.

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