Auto Encoder and Deep Auto Encoders Based Deep Learning on Medical Data
R.Subathra devi1, N.Rama2
1Mrs.R.Subathra devi, Assistant Professor, Department of computer Appli, L.N. Government College, Ponneri, (Tamil Nadu) India.
2Dr.N. Rama, Associate Professor , Department of Comp. Science, Presidency college, Chennai, (Tamil Nadu) India.
Manuscript received on November 10, 2019. | Revised Manuscript received on November 17, 2019. | Manuscript published on 30 November, 2019. | PP: 3832-3835 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8205118419/2019©BEIESP | DOI: 10.35940/ijrte.D8205.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In rapid growth of medical informatics, patient data need to be organized and used for medical diagnosis and other uses such as disease prediction and drug discovery. There are many more traditional methods used for text based information such as K-NN, K-Means and other clustering algorithms, but image based medical data (or) signals based medical data is needed. So there is a need of new approaches for efficient classification and knowledge generation process. Artificial neural network based methods are mostly suited for deep learning, since there are many more approaches available in artificial neural networks. Deep learning and Machine learning techniques requires efficient pattern or feature extraction and pattern identification. Auto encoders and deep auto encoders works based on artificial neural networks and most suitable multimodal data feature extraction and identification. In this paper we have to show deep learning methods such as auto encoder and deep auto encoders for classifying multimodal medical data.
Keywords: Auto Encoders, Deep Auto Encoders, Medical Data, Deep Learning, Medical Images.
Scope of the Article: Deep Learning.