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      Deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region

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      Cognitive Computation and Systems

      The Institution of Engineering and Technology

      learning (artificial intelligence), pattern classification, unsupervised learning, image classification, Boltzmann machines, feature extraction, medical image processing, cancer, convolutional neural nets, deep Boltzmann machine algorithm, accurate medical image analysis, deep learning algorithm, medical domain, healthcare system, deep learning framework, interest pattern, complex hyperspectral medical images, computer-aided diagnosis, hyperspectral image, post-cancerous region classification, deep Boltzmann machine architecture, bipartite structure, unsupervised generative model, deep convolutional neural network architecture, three-layer unsupervised network, backpropagation structure, image patches, discriminative classes, labelled classes, spatial information, spectral-spatial representation, cognitive computation technique, success rate DBM, complex images, traditional convolution network

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          Abstract

          In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex hyperspectral medical images is proposed. The performance of computer-aided diagnosis by verifying the region in hyperspectral image by pre and post-cancerous region classification is enhanced. For this a deep Boltzmann machine (DBM) architecture of the bipartite structure as an unsupervised generative model was developed. The performance of DBM is compared with deep convolutional neural network architecture. For implementation, a three-layer unsupervised network with a backpropagation structure is used. From the presented dataset, image patches are collected and classified into two classes, namely non-informative and discriminative classes as labelled classes. The spatial information is used for classification and spectral-spatial representation of class labels is formed. In the labelled classes, the accuracy, false-positive predictions, sensitivity are obtained for the proposed fully-connected network. By the proposed cognitive computation technique an accuracy of 95.5% with 93.5% sensitivity was obtained. From the obtained classification, accuracy and success rate DBM provide a better classification of complex images compared to traditional convolution network.

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          Most cited references 25

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          Deep Convolutional Neural Network for Inverse Problems in Imaging.

          In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on the GPU.
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            An efficient learning procedure for deep Boltzmann machines.

            We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.
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              Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

               Yoseob Han,  Jong Ye (2018)
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                Author and article information

                Contributors
                Journal
                CCS
                Cognitive Computation and Systems
                Cogn. Comput. Syst.
                The Institution of Engineering and Technology
                2517-7567
                16 August 2019
                24 September 2019
                September 2019
                : 1
                : 3
                : 85-90
                Affiliations
                Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College (Autonomous) , Sivakasi 626005, Tamil Nadu, India
                Article
                CCS.2019.0004 CCS.2019.0004.R3
                10.1049/ccs.2019.0004

                This is an open access article published by the IET in partnership with Shenzhen University under the Creative Commons Attribution-NoDerivs License ( http://creativecommons.org/licenses/by-nd/3.0/)

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