Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training

Saravanan. S,Sujitha Juliet

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.

Highlights

Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology
Experiment results prove that adapting a Deep Boltzmann machine instead of a Restricted Boltzmann machine for training the datasets achieves a better result in terms of PSNR and Structural similarity (SSIM)
Experimental results achieved for the medical image compression using deep autoencoder shows the feasibility of using a patch-based image to train the autoencoders with Deep Boltzmann Machines

Summary

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. A deep neural network that accepts the data and processes it to the other several layers and reconstructs that data is achieved by autoencoder. Deep learning is evolved as a branch of machine learning based on a neural network that processes the data and emulates the thinking process by using layers of an algorithm. Autoencoder is a kind of artificial neural network that learns the efficient data in an unsupervised way. The objective of autoencoder results in learning a representation called encoding and typically for dimensionality reduction[1] by training the network

Objectives

Methods

Results

Conclusion