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

• Published in: Biomedical signal processing and control Vol. 84; p. 104948
• Main Authors: Prakash, T. Senthil, Siva Kumar, A., Durai, C. Ramesh Babu, Ashok, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: anisotropic diffusion Kuwahara filtering; CT images; Enhanced Elman Spike Neural Network; Flamingo search optimization algorithm; Lung cancer classification; anisotropic diffusion Kuwahara filtering; Lung cancer classification; CT images; Enhanced Elman Spike Neural Network; Flamingo search optimization algorithm
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104948

•EESNN-FSOA-LCC from CT images is proposed.•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 HFLBiC for ROI region of lung cancer.•FSOAis proposed to optimize the EESNN classifier for classifying the lung cancer. 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 co-occurrence 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 OptimizationAlgorithmis 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.