Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Gray-Level Co-Occurrence Matrix (GLCM)


Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Hanan A. R. Akkar; Suhad Qasim G. Haddad

Engineering and Technology Journal, 2020, Volume 38, Issue 3B, Pages 184-196
DOI: 10.30684/etj.v38i3B.1666

Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work ( ) computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and ( ) Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis for lung.