## 1 Introduction  
In men, lung cancer follows prostate cancer as the second most prevalent type, constituting 12% of anticipated new cases. Similarly, among women, it ranks as the second most frequent cancer type after breast cancer, accounting for 13% of projected cases. Lung cancer presently stands as the primary cause of cancer-related fatalities in the USA, contributing to 21% of deaths from cancer among both genders in 2023 [1] and claims about as many lives each year than pancreatic, breast, and prostate cancer combined [1]. Low Dose Computed Tomography (LDCT) has significantly improved lung cancer diagnosis accuracy, leading to a roughly 20% reduction in mortality rates [2]. However, the widespread adoption of this screening method poses a substantial challenge to healthcare professionals due to the abundant volume of data generated from CT scans, necessitating exhaustive analysis. Furthermore, lung nodules often present similarities in shape and texture, irrespective of their benign or malignant nature, complicating the classification process and prolonging it. In response to the need to mitigate the cost, time, and risks associated with invasive treatments, automated Computer-Aided Diagnosis (CAD) systems have been proposed [3, 4]. [PERSON] et al. [5] suggested a new method to predict lung cancer called Lung-EffNet. Lung-EffNet is structured using EfficientNet as a base which was changed by adding layers on top to help improve the classification results. Lung-EffNet was tested using five different versions of EfficientNet (B0 to B4) on an open source dataset called the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) that has information about lung cancer patients, whether they have benign nodules, malignant nodules, or normal nodules. To make sure the results are fair, the data imbalance problem was fixed by using different methods of data augmentation. [PERSON] et al. [6] used CapsNet which is a supervised deep-learning model that is intended to mimic humans to find lung cancer nodules. Then, they used a pre-trained FixEfficientNet to decide if CT images show cancer or not. They trained these methods using the LUng Nodule Analysis 2016 (LUNA-16) and the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) open-source datasets and tested them out on different sets of these datasets. [PERSON] et al. [7] developed a CAD system that can find both normal and abnormal changes in lung cancer tissues in cross-sectional views. First, the authors aimed to teach the model to recognize lung cancer by comparing certain features in CT images from both patients with lung cancer and those without. Then, they tested the model on new CT scans from both groups that the framework had not seen before. The algorithm was validated on 888 CT scans from the LIDC-IDRI database that have been marked by experts. [PERSON] et al. [8] introduced a combination of a hybrid metaheuristic and Convolutional Neural Network (CNN) algorithm. Firstly, they created a CNN structure and then calculated its solution vector. This vector was then given to the Ebola Optimization Search Algorithm (EOSA) to find the best weights and biases for training the CNN to solve classification problems. [PERSON] et al. [9] proposed a Bi-level Lung Cancer Classification System on CT Scans. They introduced a new preprocessing technique called Boosted Bilateral Histogram Equalization (BBHE) that they applied prior to inputting the scans into the neural networks. For Level-1 classification, which distinguishes between nodules versus non-nodules, they introduced a Cauchy Black Widow Optimization-based CNN (CBWO-CNN). The weight updating in the CBWO-CNN employs Cauchy mutation to minimize the error rate, consequently enhancing accuracy while reducing computation time. For Level-2 classification, which distinguishes between benign and malignant nodules, they proposed a hybrid CNN model with shared parameters. This model, named SE-Xception combines the Squeeze-and-Excitation Network (SE-Net) with Xception.