Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection

The Clinical Oncology of American Society report in 2016 predicted deaths are increased upto 9570 due to oral cancer. This cancer occurs due to abnormal tissue growth in the oral cavity. This cancer has limited symptoms, so, it has been difficult to recognize in the early stages. To reduce the death...

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Bibliographic Details
Published in:Journal of medical systems 2018-11, Vol.42 (11), p.205-7, Article 205
Main Authors: Al-Ma’aitah, Mohammed, AlZubi, Ahmad Ali
Format: Article
Language:English
Subjects:
Adaptive filters
Advancements in Internet of Medical Things for Healthcare System
Algorithms
Cancer
Computer applications
Computer simulation
Gravitation
Gravity
Health Informatics
Health Sciences
Humans
Image processing
Image Processing, Computer-Assisted
Learning algorithms
Machine Learning
Markov processes
Medicine
Medicine & Public Health
Mouth Neoplasms - diagnosis
Neural networks
Neural Networks (Computer)
Optimization
Oral cancer
Oral cavity
Patient Facing Systems
Statistics for Life Sciences
Wiener filtering
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Summary:The Clinical Oncology of American Society report in 2016 predicted deaths are increased upto 9570 due to oral cancer. This cancer occurs due to abnormal tissue growth in the oral cavity. This cancer has limited symptoms, so, it has been difficult to recognize in the early stages. To reduce the death rate of this oral cavity cancer, an automatic system has been developed by applying the optimization techniques in both image processing and machine learning techniques. Even though these methods are successfully recognizing the cancer, the detection accuracy is still one of the major issues because of complex oral tissue structure. So, this paper introduces the Gravitational Search Optimized Echo state neural networks for predicting the oral cancer with effective manner. Initially the X-ray images are collected from the oral cancer database which contains several noises that has to be eliminated with the help of the adaptive wiener filter. Then the affected part has been segmented with the help of the enhanced Markov Stimulated Annealing and the features are derived from segmented region. The derived features are analyzed with the help of the proposed classifier. The excellence of the oral cancer detection system is evaluated using simulation results.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-018-1052-0