Double Integral Enhanced Zeroing Neural Network Optimized with ALSOA fostered Lung Cancer Classification using CT Images

Lung cancer is one of the deadliest diseases and the leading cause of illness and death. Since lung cancer cannot predicted at premature stage, it able to only be discovered more broadly once it has spread to other lung parts. The risk grows when radiologists and other specialists determine whether lung cancer is current. Owing to significance of determining type of treatment and its depth based on severity of the illness, critical to develop smart and automatic cancer prediction scheme is precise, at which stage of cancer. In this paper, Double Integral Enhanced Zeroing Neural Network Optimized with ALSOA fostered Lung Cancer Classification using CT Images (LCC-DIEZNN-ALSO-CTI) is proposed. Initially, input CT image is amassed from lung cancer dataset. The input CT image is pre-processing via Unscented Trainable Kalman Filtering (UTKF) technique. In pre-processing stage unwanted noise are removed from CT images. Afterwards, grayscale statistic features and Haralick texture features extracted by Adaptive and Concise Empirical Wavelet Transform (ACEWT). The proposed model is implemented on MATLAB. The performance of the proposed method is analyzed through existing techniques. The proposed method attains 18.32%, 27.20%, and 34.32% higher accuracy analyzed with existing method likes Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images Utilizing AHHMM (LCC-AHHMM-CT), Convolutional neural networks based pulmonary nodule malignancy assessment in pipeline for classifying lung cancer (LCC-ICNN-CT), Automated Decision Support Scheme for Lung Cancer Identification with Categorization (LCC-RFCN-MLRPN-CT) methods respectively.