## 2 Related Works  
This section discusses baseline approaches to machine learning-based lung nodule classification. A keyword-based string comprising machine learning and lung nodule detection approaches was used to search the papers in various databases, including IEEE, Springer, Scopus, Google Scholar, ACM, Science Direct, and Wiley. Those papers that  
Figure 2: X-rays shows no nodule (**a**), CT scan images identify the lung cancer nodules (**b**,**c**). discussed lung nodule classification and had been published in the journals listed above were selected. After initial selection, the papers were reviewed for their proposed method and classification approach. The papers that were focused on machine learning-based approaches were then identified and included in this research to learn about the foundations of lung nodule classification. All other articles retrieved from the initial search were excluded. The authors investigated the problem of lung cancer diagnosis using CT images to classify and detect pulmonary nodules. An automated deep learning-based system for classifying and detecting lung nodules was developed. Furthermore, they used novel state-of-the-art architectures such as Faster-RCNN, YOLOv3, and SSD for detection. A publicly available benchmark LIDC-IDRI dataset was used to evaluate all deep learning models. The results of the experiments revealed that the false positive rate (FPR) was reduced, and the accuracy was improved [14]. To detect nodules from CT scans using semantic segmentation in conjunction with the classification model, a novel computer-aided lung nodule detection framework was proposed (PD-CLSTM). A modified Atrous UNet + model was developed to detect nodule candidates from axial slices. The model achieves high nodule detection sensitivity by combining multilevel semantic features with ensemble skip connection [15]. According to the authors, both small-cell and non-small-cell lung cancers are quite common, although small-cell lung cancer is the more common of the two. All of these categories have the potential to result in fatalities. Small-cell lung cancer accounts for 15% of all instances of lung cancer, while non-small-cell lung cancer accounts for 85% of all lung cancer cases [16]. Pulmonary nodules are a kind of tumor that develops in the tissue of the lungs, as stated by the American Lung Association. Pulmonary nodules are most often seen in patients who have lung cancer. In 1984, a thoracic radiology terminology dictionary developed by the Fleischer Society and based on industry standards was published (public picture data source). According to the definition provided by this organization, a pleural lesion is \"any lung or pleural lesion with a well-defined, clear, generally circular opacity ranging in diameter from 2 to 30 cm\". In 2004, the association determined that a lung nodule was a \"round opacity, at least diminished, and with a maximum diameter of fewer than 3 cm\" [17]. According to the authors, a radiological opacity, known as a single pulmonary nodule (SPNs), may be as large as 30 mm in diameter and encompass about two-thirds of the lung parenchyma along with its boundaries. There are three different types of nodules: just pleural, vascularized, and plain-tailed. Contrary to a just pleural nodule, which connects to the pleural surface, a plain-tailed lesion lacks any substantial vascular connections. The types of SPNs (single pulmonary nodule) are different from each other due to their diameter. Groups of nodules larger than 30 mm are referred to as lung masses, and malignant lung tumors almost always include cancerous cells. \"Lung cancer\" refers to possible lung tissue growth [18; 19; 20]. Similarly, substantial research has been conducted on identifying and segmenting 2D and 3D CT images. Several periodicals have reported on the findings of these investigations. These approaches used for the detection of images were built by using an algorithm that often includes many steps. However, only a tiny proportion of publications regularly use the same strategies [21].