  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.
