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...
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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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