Computer-aided Diagnosis in Lung Nodule Assessment

2008 ◽  
Vol 23 (2) ◽  
pp. 97-104 ◽  
Author(s):  
Jonathan G. Goldin ◽  
Matthew S. Brown ◽  
Iva Petkovska
Keyword(s):  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

2011 ◽  
pp. 153-158
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Data Mining ◽  
Diagnostic Criteria ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Data Mining Technique ◽  
Cad Systems ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection

1998 ◽  
Vol 17 (6) ◽  
pp. 872-880 ◽  
Author(s):  
M.G. Penedo ◽  
M.J. Carreira ◽  
A. Mosquera ◽  
D. Cabello
Keyword(s):  
Neural Network ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0195875 ◽  
Author(s):  
Mizuho Nishio ◽  
Mitsuo Nishizawa ◽  
Osamu Sugiyama ◽  
Ryosuke Kojima ◽  
Masahiro Yakami ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Bayesian Optimization ◽  
Computer Aided ◽  
Aided Diagnosis

J-Net: An Adaptive System for Computer-Aided Diagnosis in Lung Nodule Characterization

2012 ◽  
pp. 25-61
Author(s):  
Massimo Buscema ◽  
Roberto Passariello ◽  
Enzo Grossi ◽  
Giulia Massini ◽  
Francesco Fraioli ◽  
...  
Keyword(s):  
Adaptive System ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Yanying Li ◽  
Shanshan Liu ◽  
Xianwen Yue
Keyword(s):  
Lung Cancer ◽  
Image Annotation ◽  
Lung Nodule ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

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