[Retracted] An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

作     者：Fayez Eid Alazemi Babar Jehangir Muhammad Imran Oh-Young Song Tehmina Karamat 

作者机构：Department of Computer Science and Information Systems College of Business Studies The Public Authority for Applied Education & Training Adailiyah 12062 Kuwait Department of Computer Science & Software Engineering Shaheed Zulfiqar Ali Bhutto Institute of Science & Technology Islamabad Pakistan Software Department Sejong University Seoul Republic of Koreasejong.ac.kr Department of Software Engineering Foundation University Islamabad Islamabad Pakistanfui.edu.pk 

出 版 物：《Journal of Healthcare Engineering》 

年 卷 期：2023年第2023卷第1期

学科分类：1001[医学-基础医学(可授医学、理学学位)] 10[医学] 

摘      要：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. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified 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 final 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%.