Abstract
Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray short term memory (LSTM) model was applied for classifying the existence of lung cancer in the CT images. Moreover, moth swarm optimization (MSO) algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate, batch size, and epoch count. For demonstrating the improved classifier results of the DLCADLC-BCT approach, a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.