Self-Upgraded Cat Mouse Optimizer with Machine Learning Driven Lung Cancer Classification on Computed Tomography Imaging

Abstract

Machine learning (ML) roles a vital play in analysing lung cancer. Lung cancer has notoriously problem to analyse but it has progressed to late phase, accomplishing the main reason for cancer-related mortality. Lung cancer can be fatal if not early treatment, and accomplishing this is a crucial problem. A primary analysis of malignant nodules is frequently developed utilizing computed tomography (CT) and chest radiography (X-ray) scans; however, the risk of benign nodules causes wrong option. During these primary steps, malignant and benign nodules seem very same. Moreover, radiologists are a hard time categorizing and observing lung abnormalities. Lung cancer screenings carried out by radiologists are frequently applied with utilize of computer-Aided diagnostic (CAD) technology. This study presents a new Self-Upgraded Cat Mouse Optimizer with Machine Learning Driven Lung Cancer Classification (SCMO-MLL2C) technique on CT images. The projected SCMO-MLL2C system mainly focuses on the identification and classification of CT images into three classes namely benign, malignant, and normal. To eradicate the noise in the CT images, the SCMO-MLL2C technique uses Gaussian filtering (GF) approach. Besides, densely connected networks (DenseNet-201) model for feature extraction process with slime mold algorithm (SMA) as a hyperparameter optimizer. In the presented SCMO-MLL2C technique, Elman Neural Network (ENN) approach was used for lung cancer classification. Furthermore, the SCMO approach has been employed for better parameter tuning of the ENN technique. To exhibit the performance validation of the SCMO-MLL2C system, the LIDC-IDRI database was utilized in this study. The simulation outcomes ensured the supremacy of the SCMO-MLL2C system over other existing approaches with maximum accuracy of 99.30%.