Open AccessLaryngeal Tumor Recognition and Classification Using Neural Networks
Author(s) -
S. Karthikeyan,
P. Farhath,
M. Vineesha,
T. Ravi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/590/1/012017
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , feature extraction , contextual image classification , image processing , segmentation , artificial neural network , feature (linguistics) , magnetic resonance imaging , process (computing) , computer vision , image (mathematics) , radiology , medicine , philosophy , linguistics , operating system
One of the highly challenging fields is Biomedical Image Processing. Image Processing involves many techniques used to detect and classify the tumor more efficiently than doctors. Manual classification is a time consuming and gives inaccurate results compared to automatic classification. Imaging methods like CT (Computer Tomography) scans, MRI (Magnetic Resonance Image), X-rays plays a major role in tumor detection and classification. Out of these MRI is widely used because it does not involve any radiation and also identifies even the smallest abnormalities present in throat. The whole process of detecting the laryngeal tumor from these scanned images can be categorised into four steps: Pre-processing, Segmentation, Feature Extraction and Classification. In proposed method, classification involves hybrid classifiers to improve accuracy.