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

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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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Acknowledgments

The Authors would like to thank the Department of Electrical Engineering, Indian Institute of Technology, Delhi and Mepco Schlenk Engineering College, Sivakasi, India for providing us the necessary facilities to carry out this research work.

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Authors and Affiliations

  1. Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India

    J. Pandia Rajan & S. Edward Rajan

  2. Vivekananda College of Engineering & Technology, Puttur, Karnataka, India

    Roshan Joy Martis

  3. Indian Institute of Technology, New Delhi, Delhi, India

    B. K. Panigrahi

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  1. J. Pandia Rajan
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  2. S. Edward Rajan
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  4. B. K. Panigrahi
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Correspondence to J. Pandia Rajan.

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Author J. Pandia Rajan declares that he has no conflict of interest. Author S. Edward Rajan declares that he has no conflict of interest. Author Roshan Joy Martis declares that he has no conflict of interest. Author B.K. Panigrahi declares that he has no conflict of interest. All authors declare that they have no conflicts of interest.

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Rajan, J.P., Rajan, S.E., Martis, R.J. et al. Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System. J Med Syst 44, 34 (2020). https://doi.org/10.1007/s10916-019-1500-5

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  • DOI: https://doi.org/10.1007/s10916-019-1500-5

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