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

Abstract

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.

Keywords

• back propagation artificial neural networks (bp-ann)
• computer tomography ct
• gray-level co-occurrence matrix (glcm)
• histogram equalization
• image pre -processing
• morphological operation