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Lung Cancer Stages Detection and Classification on CT Scan Images using Artificial Neural Network

http://hdl.handle.net/20.500.12678/0000007751
3f92c09f-24eb-41e2-9170-39373fb80274
5019834a-da2f-4c7f-9df9-b190e866c57a
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Lung Lung Cancer Stages Detection and Classification on CT Scan Images using Artificial Neural Network.jpg (3.7 MB)
Publication type
Conference paper
Upload type
Publication
Title
Title Lung Cancer Stages Detection and Classification on CT Scan Images using Artificial Neural Network
Language en
Publication date 2018-05-07
Authors
Kyawt Kyawt Htay
Nweni Kyaw
Phyo Hay Mar Wai
Hanni Htun
Description
The fundamental to the diagnosis of lung cancer in CT scans is the detection and interpretation of lung cancer nodules. Image processing techniques are widely used in many medical areas for improving earlier detection and treatment stages. This paper presents an approach for lung cancer nodule detection and stage classification on CT Scan Images. The objective of using morphological operations is to remove the imperfections in the structure of image. In this paper, morphological operations are used to detect lung cancer nodule. And then, extracts low-level features from the detected nodule. This paper uses seven features area, perimeter, eccentricity and four texture features using Gray-level Co-occurrence Matrix (GLCM). Finally, the extracted features from the detected regions are given as input to 3-layer Artificial Neural Network (ANN) classifier to classify the detected lung cancer nodule into stages. Diagnosis is mostly based on CT (computed tomography) images. The lung cancer CT scan images for each stage obtain from the internet.
Keywords
Morphological Operations, Gray-level Co-occurrence Matrix, Artificial Neural Network
Conference papers
ICBDL
2018-05-07
First International Conference on Big Data Analysis and Deep Learning Applications , ICBDL, Japan
8
Miyazaki, Japan
8
http://www.miyazaki-u.ac.jp/english/
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0
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