Lung cancer detection has been a trending research area, as automating the medical diagnosis has significant benefits. Automatic identification of lung cancer from the CT images is considered as a significant technique in recent years. Even though various techniques are developed in the literature for lung cancer detection, designing an effective technique that can automatically detect lung cancer is challenging. Hence, this research aims to develop an automated lung cancer detection scheme through deep learning and hybrid optimization algorithm. Here, the CT images from the lung cancer database are pre-processed and provided to the lung segmentation, which is carried out by active contour. Then, the nodules in the segmented image are identified using the grid-based scheme. Several features, like intensity, wavelet, and scattering transform, are mined from the segmented image and given to the proposed salp-elephant herding optimization algorithm-based deep belief network (SEOA-DBN), for the classification. Here, SEOA is newly developed by considering the qualities of salp swarm algorithm (SSA) and elephant herding optimization (EHO). For the experimentation, lung CT images are considered from the standard database and compared with the various states of art techniques. From the results, it is evident that the proposed SEOA-based DBN achieved significant performance with 96% accuracy.
Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm
