Detection and Classification of Lung Cancer Stages using Image Processing Techniques

Authors and Affiliations:
Nwe Ni Kyaw, Kyawt Kyawt Htay, Hanni Htun, Faculty of Computer Science, Myanmar Institute of IT, Mandalay, Myanmar
Kyaw Kyaw Naing, Physics Department, Shwebo University, Shwebo, Myanmar
Phyu Myo Thwe, Faculty of Computer Science, Computer University, Kalay, Myanmar


Abstract:
In current days, image processing techniques are widely used in many medical areas for improving earlier detection and treatment stages, especially in various cancer nodules such as the lung cancer, breast cancer, brain cancer and so on. This paper shows the detection and classification of lung cancer stages based on CT Scan Images. The median filter algorithm is used for image processing. 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:
Preprocessing, Morphological Operations, Gray Level Co-occurrence Matrix, Artificial Neural Network.


Cite This Article: 
Nwe Ni Kyaw, Kyaw Kyaw Naing, Phyu Myo Thwe, Kyawt Kyawt Htay, Hanni Htun, "Detection and Classification of Lung Cancer Stages using Image Processing Techniques", International Journal of Electrical Electronics & Computer Science Engineering, Volume 6, Issue 4, pp. 01-06. 

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