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

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

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 diagno...

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Bibliographic Details
Main Authors: Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:Cognitive Computation and Systems
Subjects:
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
Online Access:https://digital-library.theiet.org/content/journals/10.1049/ccs.2019.0004

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https://digital-library.theiet.org/content/journals/10.1049/ccs.2019.0004

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