Research Article
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training
@ARTICLE{10.4108/eai.24-9-2020.166360, author={Saravanan. S and Sujitha Juliet}, title={Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={6}, number={24}, publisher={EAI}, journal_a={PHAT}, year={2020}, month={9}, keywords={Medical Image compression, Deep Learning, Deep Autoencoder, Deep Boltzmann Machines}, doi={10.4108/eai.24-9-2020.166360} }- Saravanan. S
Sujitha Juliet
Year: 2020
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training
PHAT
EAI
DOI: 10.4108/eai.24-9-2020.166360
Abstract
INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology. Autoencoder, an unsupervised learning algorithm with the input value as same as that of the output value, is considered in this research work for effective medical image reconstruction.
OBJECTIVES: Medical data needs to be reconstructed without distorting the details present over it. A deep neural network that accepts the data and processes it to the other several layers and reconstructs that data is achieved by autoencoder.
METHODS: Deep Autoencoder is implemented in this methodology as it has been considered for high dimensionality reduction. Layer by layer pretraining is achieved using an approximate inference algorithm called Deep Boltzmann Machine.
RESULTS: The proposed method proves to be efficient when compared with the performance of the other autoencoders such as Deep Autoencoder with multiple Backpropagation (DA-MBP), Deep Autoencoder with RBM (DA-RBM) and Deep Convolutional Autoencoder with RBM (DCA-RBM).
CONCLUSION: Performance metrics are measured in terms of Mean Square Error, Structural similarity Index and PSNR.
Copyright © 2020 Saravanan.S et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
