## 1 Introduction  
Currently, lung cancer is the leading cause of cancer-related mortality across the globe, while in underdeveloped and developed countries, it records a low survival rate after diagnosis [1]. Based on the present statistics, the 5-year survival rate is around 16%; it is predicted that there will be 12 million cancer-related deaths per year by the end of 2020, with highest percentage of these being lung cancer-related deaths [2]. When nodules are diagnosed at an early stage, the survival rate can be enhanced. Lung nodules are abnormally growing tissues that represent lung cancer. Typically, the nodules are spherical or round in shape, with a diameter of 30 mm. Medical images are captured using different imagingmodalities [3]. Amongst others, computed tomography (CT) is a basic imaging approach for screening lung nodules, whereas other available approaches are of less significance [4]. The computer-aided detection (CAD) technique assists in early diagnoses of lung cancer [5]. The primary objective of CAD is to recognize and extract the regions of interest (ROI) from the images attained using imaging modalities. The segmentation of lung tissue from chest images during the preprocessing stage is emerging in the CAD scheme, so as to reduce the searching region for lung nodules [6]. Then, the other essential steps in this process are the segmentation and detection of lung nodules in the presented searching space. Finally, the nodules are classified as benign or malignant [7]. In the CAD technique, the final phase is the main element for diagnosis and recognition of lung nodules from CT. In CAD systems for diagnosis, a classification module classifies the nodule candidates, recognized during the preceding phase, into nodules or non-nodules (i.e., based on their anatomic structure). Further, the CAD scheme for diagnoses (also known as CADx) categorizes the diagnosed nodules (whether by a radiologist or a computer) into benign or malignant [8]. CAD is a computerized process for pathological diagnoses of malignant tumors, and it belongs to the class of artificial intelligence (AI). This technique helps in analyzing tumor lesions through medical image processing technique and other possible biochemical and physiological methods integrated with calculation and analysis. In the healthcare sector, AI transforms qualitative subjective image data into quantitative objective image data, and assists the clinician in increasing the precision of medical decision-making [9]. Deep learning (DL) is a subdivision of AI which automatically extracts the discriminatory data features from datasets. With simple extraction method, the performance can be adapted systematically and easily [10]. Among the available DL models, the convolutional neural network (CNN) model has shown better performance than other DL architectures. Many machine learning (ML) models necessitate the investigation of relationship of data with class labels in order to extract the optimal features for classification. The outcome of the classifier is decided by the features being chosen. Lung cancer classification using the CNN model provides better results than other DL models such as auto encoders; as such, many studies have started to focus on enhancing the CNN model. It has the ability to classify biomedical images with improved accuracy over handcrafted features such as histogram, shape, texture, and so on. Numerous ML models, specifically DL models, have been employed for lung cancer classification. Several ML algorithms, for instance the deep learning algorithm, have been used to accomplish this task, but without guaranteed accuracy. Therefore, there is still room for improvement. In this background, the current research article presents the cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. In the proposed CHO-CADLCC technique, first the data undergo pre-processing using the Gabor filtering-based noise removal process.