Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality me...

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Vydáno v:Journal of medical systems Ročník 44; číslo 2; s. 34
Hlavní autoři: Rajan, J. Pandia, Rajan, S. Edward, Martis, Roshan Joy, Panigrahi, B. K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2020
Springer Nature B.V
Témata:
Accuracy
Artificial neural networks
Cancer
Classification
Cloud computing
Decision making
Health care
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Image & Signal Processing
Internet of Things
Medicine
Medicine & Public Health
Neural networks
Oral cancer
Recent Advances in Deep Learning for Biomedical Signal Processing
Statistics for Life Sciences
Training
Computer vision
IoT architecture
Deep convolutional neural network
Medical image analysis
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí:Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1500-5