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 ca...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2020-12-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_169531_adba63167ef8bc969b0357a10e1e20ff.pdf |
| _version_ | 1797336384763068416 |
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| author | Hanan Akkar Suhad Qasim Haddad |
| author_facet | Hanan Akkar Suhad Qasim Haddad |
| author_sort | Hanan Akkar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T08:53:34Z |
| format | Article |
| id | doaj.art-96aee5f13b294365a818e0703a12eaa3 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T08:53:34Z |
| publishDate | 2020-12-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-96aee5f13b294365a818e0703a12eaa32024-02-01T07:36:38ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582020-12-01383B18419610.30684/etj.v38i3B.1666169531Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network AlgorithmHanan Akkar0Suhad Qasim Haddad1Department of Electrical Engineering, University of Technology, Baghdad, IraqAsst.Lecturer, Department of Computer Engineering, University of Technology, Baghdad ,Iraq,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.https://etj.uotechnology.edu.iq/article_169531_adba63167ef8bc969b0357a10e1e20ff.pdfback propagation artificial neural networks (bp-ann)computer tomography ctgray-level co-occurrence matrix (glcm)histogram equalizationimage pre -processingmorphological operation |
| spellingShingle | Hanan Akkar Suhad Qasim Haddad Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm Engineering and Technology Journal back propagation artificial neural networks (bp-ann) computer tomography ct gray-level co-occurrence matrix (glcm) histogram equalization image pre -processing morphological operation |
| title | Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm |
| title_full | Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm |
| title_fullStr | Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm |
| title_full_unstemmed | Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm |
| title_short | Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm |
| title_sort | diagnosis of lung cancer disease based on back propagation artificial neural network algorithm |
| topic | back propagation artificial neural networks (bp-ann) computer tomography ct gray-level co-occurrence matrix (glcm) histogram equalization image pre -processing morphological operation |
| url | https://etj.uotechnology.edu.iq/article_169531_adba63167ef8bc969b0357a10e1e20ff.pdf |
| work_keys_str_mv | AT hananakkar diagnosisoflungcancerdiseasebasedonbackpropagationartificialneuralnetworkalgorithm AT suhadqasimhaddad diagnosisoflungcancerdiseasebasedonbackpropagationartificialneuralnetworkalgorithm |