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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things

J Pandia Rajan1, S Edward Rajan2, Roshan Joy Martis3

  • 1Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India. pandiarajan@mepcoeng.ac.in.

Journal of Medical Systems
|December 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using modified vesselness measurement and Deep Convolutional Neural Networks (DCNN) for accurate oral cancer detection in smart healthcare systems. The approach achieves high accuracy, aiding in critical pre-interventional decision-making for oral cancer treatment.

Keywords:
Computer visionDeep convolutional neural networkIoT architectureMedical image analysis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Oral cancer diagnosis relies heavily on accurate image analysis for effective treatment planning.
  • Integrating Internet of Things (IoT) into healthcare offers potential for advanced e-healthcare solutions.
  • Automated pattern analysis of cancerous tissue is crucial for early detection and intervention.

Purpose of the Study:

  • To propose a novel method for identifying oral cancer regions using modified vesselness measurement and Deep Convolutional Neural Networks (DCNN).
  • To enhance the accuracy of oral cancer detection within an IoT-based smart healthcare system.
  • To improve pre-interventional decision-making for oral cancer treatment through precise image analysis.

Main Methods:

  • Utilized a modified vesselness measurement technique for robust noise handling and structure preservation.
  • Employed a Deep Convolutional Neural Network (DCNN) for classification of oral cancer regions.
  • Integrated multi-dimensional information from feature vector selection for improved classification accuracy.
  • Extracted marked feature vector points from connected components for CNN training and individual analysis.

Main Results:

  • Achieved an accuracy of 96.8% and a sensitivity of 92% on a dataset of 1500 images.
  • Demonstrated effective noise handling and preservation of small structures with the vesselness filtering scheme.
  • Showcased improved classification accuracy through the DCNN framework by deblurring regions of interest.

Conclusions:

  • The proposed algorithm is effective and accurate for classifying oral cancer regions, supporting precise decision-making.
  • The developed system is suitable for IoT-based healthcare diagnosis requiring accuracy and real-time capabilities.
  • This research validates the potential of AI and IoT in advancing oral cancer detection and management.