A Novel Approach based on Average Information Parameters for Investigation and Diagnosis of Lung Cancer using ANN

Abstract

In this paper, an average informational parameter based approach for lung cancer detection and diagnosis has been proposed. Suggested methodology is established on average information parameters by utilizing image processing tools for lung cancer investigation. The real issue with the lung cancer diseases is the time constraint for physical diagnosis that expands the death possibilities. Henceforth essentially proposed technique is an approach that would help the medical practitioners for precise and superior decision against the lung cancer discovery. The crucial point in the proposed method is that it helps the doctors for taking a firm decision on lung cancer diagnosis. Microscopic lung images are taken for analysis and investigation by using digital image processing techniques which also recovers the quality of images that has been degraded by several reasons including random noise. The statistical parameters are implemented for lung cancer analysis. The statistical and mathematical parameters are implemented like Entropy, Standard Deviation, Mean, Variance and MSE under average information method. The statistical range of each parameter is calculated for number of iterations. The individual statistical parameter analysis with its impact on lung cancer images is carried out and finally the Artificial Neural Network is the final decision maker in lung cancer diagnosis. This paper also rejects the null hypothesis test by implementing one of the standard statistical methods.