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.
Highlights
Lung cancer is defined as an uncontrolled cell growing in the tissues of lung, which can be identified as tumor in lung
Various soft computing algorithms were utilized in previous years by researchers to determine and enhance the diagnosis degree of cancer cells in a medical image in order to present a simple design, less budget, time saving in addition to giving hopeful result for patients in rural places [2]
In order to give more accuracy and reliability to the work the network was training with real images of scanned lung images which were obtained from Baghdad educational hospital / Medical city/ Radiology Institute, with the assist of which is specialized site for viewing medical images, we could save the images in order to prepare them for training and testing phases
Summary
Lung cancer is defined as an uncontrolled cell growing in the tissues of lung, which can be identified as tumor in lung. . Various soft computing algorithms were utilized in previous years by researchers to determine and enhance the diagnosis degree of cancer cells in a medical image in order to present a simple design, less budget, time saving in addition to giving hopeful result for patients in rural places [2]. In order to give more accuracy and reliability to the work the network was training with real images of scanned lung images which were obtained from Baghdad educational hospital / Medical city/ Radiology Institute, with the assist of (https://www.radiantviewer.com) which is specialized site for viewing medical images, we could save the images in order to prepare them for training and testing phases. Section five will discuss the result of the work, besides that, comparison was done between the suggested technique and previous works section six gives the conclusion
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