Diagnosis of lung cancer disease based on back-propagation artificial neuralnetwork algorithm
Joint Authors
Haddad, Suhad Q. G. H.
Akkar, Hanan Abd al-Rida
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 3B (31 Mar. 2020), pp.184-196, 13 p.
Publisher
Publication Date
2020-03-31
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Topics
Abstract EN
Early stage detection of lung cancer is important for successful controlling of the diseases, alsoto 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 BackPropagation 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 determinethe 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 wereapplied toget best featuresextraction 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 withaccuracy rate 95.9 % intrainlm activation function.
.
GraphicUser Interface ( )was displaying to show the final diagnosis Early stage detection of lung cancer is important for successful controlling of the diseases, alsoto 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 BackPropagation 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 determinethe 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 wereapplied toget best featuresextraction 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 withaccuracy rate 95.9 % intrainlm activation function.
.
GraphicUser Interface ( )was displaying to show the final diagnosis forlung
American Psychological Association (APA)
Akkar, Hanan Abd al-Rida& Haddad, Suhad Q. G. H.. 2020. Diagnosis of lung cancer disease based on back-propagation artificial neuralnetwork algorithm. Engineering and Technology Journal،Vol. 38, no. 3B, pp.184-196.
https://search.emarefa.net/detail/BIM-1020784
Modern Language Association (MLA)
Akkar, Hanan Abd al-Rida& Haddad, Suhad Q. G. H.. Diagnosis of lung cancer disease based on back-propagation artificial neuralnetwork algorithm. Engineering and Technology Journal Vol. 38, no. 3B (2020), pp.184-196.
https://search.emarefa.net/detail/BIM-1020784
American Medical Association (AMA)
Akkar, Hanan Abd al-Rida& Haddad, Suhad Q. G. H.. Diagnosis of lung cancer disease based on back-propagation artificial neuralnetwork algorithm. Engineering and Technology Journal. 2020. Vol. 38, no. 3B, pp.184-196.
https://search.emarefa.net/detail/BIM-1020784
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 195-196
Record ID
BIM-1020784