Enhanced Elman spike Neural network optimized with flamingo search optimization algorithm espoused lung cancer classification from CT images

At present, researchers have been try to enhance the CAD system performance utilizing deep learning techniques in lung cancer screening and computed tomography (CT), but none of them attain the adequate accuracy. Therefore, an Enhanced Elman Spike Neural Network optimized with flamingo search optimization algorithm espoused Lung cancer classification from CT images (EESNN-FSOA-LCC) is proposed in this article. Initially, the input lung CT images are gathered through IQ-OTH/NCCD Lung Cancer Dataset. Then the input lung CT images are pre-processed using anisotropic diffusion Kuwahara filtering. These pre-processed lung CT images are fed to Hesitant Fuzzy Linguistic Bi-objective Clustering process for ROI region of lung cancer. Then the significant features present under ROI region of lung cancer segmentation are clarified through the help of Gray level cooccurrence matrix (GLCM) window adaptive approach. The extracted features are presented to EESNN for categorizing lung CT images image as normal, Benign, and Malignant. In general, EESNN classifier not divulges any adaption of optimization methods for determining the optimum parameters and to assure exact classification of lung cancer. Hence, a flamingo search Optimization Algorithm is proposed to optimize the EESNN classifier, which precisely classifies the lung cancer. The proposed EESNN-FSOA-LCC approach is activated in python. The proposed EESNN-FSOA-LCC approach achieves 38.58%, 25.69% and 43.87%, high accuracy when comparing to the existing CTI-LCC-SVM, CTI-LCC-GoogleNet-DNN and CTI-LCC-CNN respectively.

Automated summary

In this article, an Enhanced Elman Spike Neural Network optimized with flamingo search optimization algorithm is proposed for lung cancer classification from CT images. The proposed method preprocesses the lung CT images, extracts features using Hesitant Fuzzy Linguistic Bi-objective Clustering and Gray level cooccurrence matrix techniques, and then classifies the images using the EESNN model. Experimental results demonstrate that the proposed method achieves high accuracy in lung cancer classification.