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

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.

Keywords

• 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