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Multi-View Auxiliary Diagnosis Algorithm for lung nodules

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Computers, Materials & Continua 2022年第9期72卷 4897-4910页

作者： Shi Qiu Bin Li Tao Zhou Feng Li Ting Liang Key Laboratory of Spectral Imaging Technology CAS Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an710119P.R.China School of Information Science and Technology Northwest UniversityXi’an710127P.R.China School of Computer Science and Engineering North Minzu UniversityYinchuan750021P.R.China Institute of Education University College LondonLondonThe United Kingdom Department of Radiology The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an10061P.R.China

lung is an important organ of human *** and more people are suffering from lung diseases due to air *** diseases are usually highly *** as lung tuberculosis,novel coronavirus COVID-19,*** nodule is a kind of high-density globular lesion in the *** need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis,which is *** this reason,the use of computer-assisted diagnosis of lung nodules has become the current main *** the process of computer-aided diagnosis,how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current *** solve this problem,we propose a three-dimensional optimization model to achieve the extraction of suspected regions,improve the traditional deep belief network,and to modify the dispersion matrix between *** construct a multi-view model,fuse local three-dimensional information into two-dimensional images,and thereby to reduce the complexity of the *** alleviate the problem of unbalanced training caused by only a small number of positive *** show that the false positive rate of the algorithm proposed in this paper is as low as 12%,which is in line with clinical application standards.

关键词： lung nodules deep belief network computer-aided diagnosis multi-view

AN IMPROVED RANDOM WALK SEGMENTATION ON THE lung nodules

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International Journal of Biomathematics 2013年第6期6卷 105-120页

作者： LI GUO YUNTING ZHANG ZEWEI ZHANG DONGYUE Li School of Medical Imaging Tianjin Medical University Tianjin 300203 P. R. China General Hospital Radiology Tianjin Medical University Tianjin 300203 P. R. China

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM （Fuzzy C Means） segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

关键词： lung nodules Gabor FCM ellipse fitting random walk.

A Survey of lung nodules Detection and Classification from CT Scan Images

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Computer Systems Science & Engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed Fazli Subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： lung nodules computed tomography scans lung cancer deep learning

Clinical and Pathological Research Status of Multiple Pulmonary nodules

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Journal of Cancer Therapy 2023年第4期14卷 170-181页

作者： Yun Wang Shiqi Song Jian Huang Department of Pathological Diagnosis and Research Center The Affiliated Hospital of Guangdong Medical University Zhanjiang China Guangzhou Huayin Health Medical Group Co. Ltd. Guangzhou China

With the changes in disease spectrum and the popularization of screening of low-dose spiral CT (CT) in the chest, more and more pulmonary nodules have been detected, most of which are bipulmonary multiple nodules. The existence of multiple pulmonary nodules means that it may be a pathological state of benign and malignant co-existence. The origin and evolution of pulmonary nodules in different histopathological states have a great impact on the choice of treatment methods. In recent years, the rise of immunotherapy has brought a breakthrough in the treatment of refractory lung cancer. However, some patients are still ineffective in immunotherapy, which may be related to the immune microenvironment where nodules are proportioned in different components in different pathological states. This review article mainly predicts the development process of nodules by analyzing the origin of multiple pulmonary nodules and the immune microenvironment of nodules in different pathological conditions, so as to provide guidance for clinical treatment.

关键词： lung nodules Originate Pathology Immune Microenvironment

Role of inflammatory markers in the evaluation of indeterminate pulmonary nodules:a review

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Clinical Research Communications 2024年第3期7卷 48-52页

作者： Qiao-Li Wang Xiao-Qin Liu Ting Wang Department of Health Screening Center Deyang Peoples'HospitalDeyang 618000China.

The global mortality rate of lung cancer surpasses that of any other type of cancer,establishing it as the foremost cause of cancer-related deaths *** objectives of lung nodule assessment are to expedite the diagnosis and treatment of patients with malignant nodules while minimizing unnecessary diagnostic procedures for those with benign *** systemic inflammatory response is closely linked to *** levels of inflammatory markers and their derived parameters markers including neutrophil-to-lymphocyte ratio(NLR),lymphocyte-to-monocyte ratio(LMR),monocyte-to-albumin ratio(MAR),platelet-to-lymphocyte ratio(PLR)and systemic immune‐inflammation index(SII),closely associated with lung *** objective of this article is to comprehensively evaluate the differentiation between benign and malignant pulmonary nodules in terms of inflammatory response indicators,aim at provide practical recommendations for the clinical diagnosis,treatment,and management of pulmonary nodules.

关键词： lung nodules inflammatory makers lung cancer

Attention Based Multi-Patched 3D-CNNs with Hybrid Fusion Architecture for Reducing False Positives during lung Nodule Detection

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Journal of Computer and Communications 2021年第4期9卷 1-26页

作者： Vamsi Krishna Vipparla Premith Kumar Chilukuri Giri Babu Kande Emerging Technologies Department Mahindra & Mahindra IT Mumbai Maharashtra India Computer Vision Division Supervue AI Visakhapatnam Andhra Pradesh India Department of Electronics & Communication Engineering Vasireddy Venkatadri Institute of Technology Nambur Andhra Pradesh India

In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.

关键词： 3D-CNN Attention Gated Networks lung nodules Medical Imaging X-Ray Computed Tomography