A Framework for Medical Data Analysis Using Deep Learning Based on Conventional Neural Network (CNN) and Variable Auto-Encoder
Shaik Shabbeer1, E. Srinivasa Reddy2

F Shaik. Shabbeer is a research scholar in College of Engineering & Technology at Acharya Nagarjuna University and he did M. Tech (Computer Science & Engineering) Degree from JNTU-Hyderabad.
Dr. E. Srinivasa Reddy, Professor & Dean R&D in University Engineering College of Acharya Nagarjuna University. Guntur, Andhra Pradesh
Manuscript received on 1 August 2019. | Revised Manuscript received on 8 August 2019. | Manuscript published on 30 September 2019. | PP: 852-857 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4038098319/19©BEIESP | DOI: 10.35940/ijrte.C4038.098319
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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: 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.
Keywords: Conventional Neural Network, Auto-Encoder, Medical Data, MIMIC3.

Scope of the Article:
Distributed Sensor Networks