Diagnosis of lung cancer disease based on back-propagation artificial neuralnetwork algorithm

الملخص 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.

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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