Special Issue "Healthcare Intelligence in Cancer Prognosis and Prediction"

Submission Deadline: 30 November 2021 (closed)
Guest Editors
Dr. A. Balasundaram, Vellore Institute of Technology (VIT), India.
Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.
Dr. Kong Fah Tee, University of Greenwich, United Kingdom.

Summary

Cancer is a chronic disease characterized by the development and growth of abnormal cells that multiply in an uncontrolled manner and possess the ability to infiltrate and destroy normal tissues. It is considered as one of the prominent diseases leading to death. Early detection and effective prognosis may suppress the severity of this disease and might prove pivotal in saving human lives. Cancer prognosis is influenced by several factors such as cancer type, location, level of infection, grade, patient age and physical and general health etc. The culmination of proper analysis of all these aspects results in effective cancer prognosis and prediction. To enable automated, reliable and accurate solutions, Artificial Intelligence and its allied sub domains such as machine learning, computer vision, data analytics, pattern mining, pattern recognition etc., are considered as viable technology enablers that promote effective Cancer Prognosis and Prediction.

 

This special issue is focused towards providing selected works that discuss about artificial intelligence based solutions for early cancer prognosis and prediction.


Keywords
Machine Learning, Cancer Prediction, Internet of Things, Deep Neural Network, Computer Vision

Published Papers
  • Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features
  • Abstract Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency sub-bands… More
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