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Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm | ||
| Engineering and Technology Journal | ||
| Article 11, Volume 38, 3B, 2020, Pages 184-196 PDF (1.59 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.v38i3B.1666 | ||
| Authors | ||
| Hanan A. R. Akkar1; Suhad Qasim G. Haddad* 2 | ||
| 1Department of Electrical Engineering, University of Technology, Baghdad, Iraq | ||
| 2Asst.Lecturer, Department of Computer Engineering, University of Technology, Baghdad ,Iraq, | ||
| 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 | ||
| References | ||
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