ResearchArticle An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique Fayez Eid Alazemi, 1 Babar Jehangir, 2 Muhammad Imran, 2 Oh-Young Song , 3 and Tehmina Karamat 4 1 Department of Computer Science and Information Systems, College of Business Studies, Te Public Authority for Applied Education & Training, Adailiyah 12062, Kuwait 2 Department of Computer Science & Software Engineering, Shaheed Zulfqar Ali Bhutto Institute of Science & Technology, Islamabad, Pakistan 3 Software Department, Sejong University, Seoul, Republic of Korea 4 Department of Software Engineering, oundation University Islamabad, Islamabad, Pakistan Correspondence should be addressed to Oh•Young Song; oysong@sejong.edu Received 17 March 2022; Revised 14 May 2022; Accepted 24 November 2022; Published 4 February 2023 Academic Editor: Ali Kashif Bashir Copyright © 2023 Fayez Eid Alazemi et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient’s survival rate. Te corresponding work presents the method for improving the computer•aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. Te main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classifcation, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the frst step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identifed and segmented with an optimal threshold value and rule•based trimming. It also selects 2D and 3D features from the candidate segmented node. In the fnal step, the selected features are used to train the SVM and classify the nodes and classify the non•nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%. 1. Introduction 1.1.SignifcanceofStudy. In the current era, health care is an important domain in which a lot of research work has been carried out, various researchers working in the healthcare domain to solve the problems of healthcare applications, Sultan et al. 1] introduced a hybrid approach for Alzheimer patients through video summarization. Another research work in the health care domain is carried out by Bacanin et al. 2]; the authors used wireless sensing network tech• nology to monitor human health, pollution predictions, and some other related factor that are useful for human health. Artifcial Intelligence and machine learning techniques are very commonly used in the health care sector and researchers get very good results. An artifcial intelligence• based technique by Chang et al. 3] was introduced; they ofer a drug selection framework for the individualized selection of NSCLC patients using an artifcial intelligence• assisted medical system. Te method forecasts drug efectiveness•cost under the concept of ensuring efcacy while taking the economic cost of targeted drugs into ac• count as an auxiliary decision•making element. Similarly Ramzan et al. 4] introduce a protection system for medical images; the author proposed a technique in which they secure medical images. Another medical base Optimal feature extraction and ulcer classifcation from WCE image data using deep•learning technique in introduced by 5] and another medical image•related work has been carried out by Hindawi Journal of Healthcare Engineering Volume 2023, Article ID 8262741, 11 pages https://doi.org/10.1155/2023/8262741