Abstract
ABSTRACT Lung cancer starts in the lungs and spreads to other organs in the body. Premature identification can only help the doctor to make an exact diagnosis and it may save the life of patients. Numerous studies have been conducted in this area, but none of them attains the accuracy outcomes. To overcome this drawback, a deep multi-scale three dimensional convolutional neural network optimized with the manta ray foraging optimization algorithm is proposed in this article for lung cancer classification on CT images (LCCT-DMS3DCNN-MRFOA) which effectively classifies the lung cancer as benign or malignant. The simulation is executed in MATLAB. The ELCAP dataset is utilized to confirm the performance of the proposed LCCT-DMS3DCNN-MRFOA approach. The efficiency of the LCCT-DMS3DCNN-MRFOA approach attains 14.21%, 22.96% and 24.94% higher accuracy; 12.59%, 11.71% and 11.55% higher AUC and 59.83%, 53.05% and 61.41% lower computational time compared with existing methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have