Open AccessAuto Encoder and Deep Auto Encoders Based Deep Learning on Medical Data
Author(s) -
Monisha Devi,
DR.N. Rama
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8205.118419
Subject(s) - deep learning , autoencoder , artificial intelligence , computer science , artificial neural network , feature extraction , cluster analysis , encoder , identification (biology) , machine learning , pattern recognition (psychology) , feature learning , feature (linguistics) , linguistics , philosophy , botany , biology , operating system
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.