| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560755 |
| ISSN: | 1405-5546 |
| Autores: | Mansingh, Padmini1 Pattanayak, Binod Kumar1 Pati, Bibudhendu2 |
| Instituciones: | 1Deemed to be University, Department of Computer Science and Engineering, Bhubaneswar, Odisha. India 2Ramadevi Women's University, Department of Computer Science, Bhubaneswar, Odisha. India |
| Año: | 2022 |
| Periodo: | Oct-Dic |
| Volumen: | 26 |
| Número: | 4 |
| Paginación: | 1491-1501 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | 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. |
| Keyword: | Deep learning, Big data analytics, CANN, ICA, Healthcare, Machine learning |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
