phat 18: e2

Research Article

Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training

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  • @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: Online First},
        volume={},
        number={},
        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
Saravanan. S1,*, Sujitha Juliet1
  • 1: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
*Contact email: saranrulz671@gmail.com

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.