Open AccessA Framework for Medical Data Analysis using Deep Learning Based on Conventional Neural Network (CNN) and Variable Auto-Encoder
Author(s) -
F Shaik. Shabbeer*,
E. Srinivasa Reddy
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.c4038.098319
Subject(s) - computer science , convolutional neural network , artificial intelligence , encoder , deep learning , artificial neural network , task (project management) , autoencoder , mechanism (biology) , machine learning , pattern recognition (psychology) , variable (mathematics) , data mining , engineering , mathematical analysis , philosophy , mathematics , systems engineering , epistemology , operating system
Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.