Identification of lung cancer using archimedes flow regime optimization enabled deep belief network.

The cancer in lungs is termed as a severe disease that can lead to huge deaths globally. The timely identification can be useful to increase rate of survival. A Computed Tomography is used to identify position of tumor and discover level of cancer. The present study provides an innovative model with CT to identify lung cancer. The lung CT is used for analyzing the cancer levels. These images undergoes pre-processing wiener filter to eradicate the noise. Subsequently, the lung lobe segmentation is attained with LadderNet whose training is executed with Archimedes Flow Regime Optimization (AFRO). The detection of nodule is performed using grid based strategy followed by extraction of statistical, GLCM and texture features. Finally, lung cancer detection are performed using DBN and the hyper parameters of DBN are optimally fine tuned using devised AFRO. The AFRO-based DBN provided finest efficiency with accuracy of 95.8%, F-measure of 92.6% and precision of 93.6%.The finest outcomes revealed that the technique has feasible robustness in classifying the nodules. The technique poses the ability to help the radiologists to interpret the data more effective and distinguish the lung nodules with CT images. [ABSTRACT FROM AUTHOR]