Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm
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.
How to Cite
The author assigns to Engineering and Technology Journal with full title guarantee, all copyrights, rights in the nature of copyright, and all other intellectual property rights in the article throughout the world (present and future, and including all renewals, extensions, revivals, restorations and accrued rights of action). The Author represents that he/she is the author and proprietor of this Article and that this Article has not heretofore been published in any form. The Author warrants that he/she has obtained written permission and paid all fees for use of any literary or illustration material for which rights are held by others. The author agrees to hold the editor(s)/publisher harmless against any suit, demand, claim or recovery, finally sustained, by reason of any violation of proprietary right or copyright, or any unlawful matter contained in the submitted article.