Abstract: This study is an algorithm based medical image processing to detection lung cancer in CT images. The detection system comprises of different stages to finally reach its target which is todetect lung tumor. The beta function used for properties lung cancer in CT images analysis because the role of that function in the accurate stages analysis. Statistical properties were determined maintaining of 7 properties: entropy, median, mean, contrast, correlation, energy and homogeneity. These statistical properties have proved to be accurate in determining the difference between time periods for lung cancer. We used the function of the persistence of the function as it helps in the analysis of areas infected with high accuracy. The results showed orthogonal conversion has been efficient in analyzing the area of pixels without affecting the parameters of the image and determine the stages of disease.
Shaymaa Maki Kadham, Hind Rustum Mohammed and Hawraa Saheb Abo Hamed, 2019. Conversion of Orthogonal Beta Function for Analysis Detection Lung Cancer Stages. Journal of Engineering and Applied Sciences, 14: 5917-5924.