### Related Works  
Today, CAD-based systems are regularly used to detect and diagnose numerous abnormalities in clinical practice. The efficiency of these systems is directly related to their disease prediction accuracy. Different image processing and machine learning algorithms are utilized in these systems to analyze the medical scans. Approximately 20% of the total CAD-based medical diagnostic systems are used for lung cancer diagnosis [15]. Hence, an effective framework to segment and classify pulmonary nodules from CT scan images seems to be a viable solution. It consists of multiple phases. Image pre-processing improves the quality of the image by removing artifacts, thus producing better results in the following steps. Segmentation makes the analysis of images easier by differentiating the subjects from the background. After that, extracting features such as the intensity, texture, and color of each pixel helps to represent every image uniquely in terms of mathematical parameters, which can be used to classify the image into different categories. In a study conducted by [PERSON] et al. [16], CT scan images were used with and without contrast medium and utilized texture features to train the SVM classifier. The SVM classifier was trained with the obtained features to classify the nodules into benign and malignant categories. [PERSON] et al. [17] explored various evolutionary image segmentation algorithms for the detection of lung cancer in CT scan images. The adaptive median filter had better pre-processing capabilities compared to the mean and median filters. The pre-processed image was segmented using four segmentation algorithms, i.e., k-means clustering, particle swarm optimization, inertia weighted PSO, and Guaranteed Convergence PSO (GCPSO), and classification accuracy was reported as 0.885, 0.89, 0.889, and 0.958, respectively. A novel method to automatically detect and classify solitary lung nodules from CT scan images has been suggested [18], and the input CT scan images were denoised using a bilateral filter and thresholding, and morphology was applied for segmentation. The intensity and geometric features were extracted, and a Bayesian classifier, to reduce the time required for training, was employed to define the pulmonary nodules either as benign or malignant. [PERSON] et al. [19] used CT scan data stored in DICOM format to analyze lung nodules and detect the presence of lung cancer. Raw pixel data from DICOM files were processed by removing unnecessary portions using Thresholding and Connected component analysis. A Convolutional Neural Network (CNN) with an Adam optimizer and ReLU activation function was trained to extract features from the images and classify them as cancerous or non-cancerous. The model, which was used on the data of 20 patients, achieved an accuracy of 65%. The MRI and CT scan images were fused together based on DWT for better details and pre-processed using a median filter and were segmented with the region of interest method and [PERSON]'s thresholding [20]. The feature extraction and classification of lungs into normal and abnormal was carried out using K-Nearest Neighbor (KNN). Further, region growing and morphology were applied to extract the tumors in abnormal lung images, which were classified as benign or malignant with the help of an Artificial Neural Network (ANN). The NSCLC history classification was analyzed using different classifiers, such as radionics with KNN and Support Vector Machine (SVM) classifiers, CNN classifiers, CNN with Long Short-Term Memory (LSTM) classifiers, and CNN with LSTM and radiomics classifiers [21]. In this method, the clinical decision was made only with the help of radiological findings. [PERSON] et al. [22] delineated a classification model that used a semi-automatic technique for segmentation by taking a single seed from the user, and different shapes, margins, and texture-based features were calculated to represent the pulmonary nodules.