scholarly journals Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Panpan Wu ◽  
Xuanchao Sun ◽  
Ziping Zhao ◽  
Haishuai Wang ◽  
Shirui Pan ◽  
...  
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Lung Nodule ◽  
Residual Network ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Positive Rate ◽  
And Migration ◽  
Nodule Classification

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.

scholarly journals Development of CAD System for Automatic Lung Nodule Detection: A Review

2021 ◽  
Vol 41 ◽  
pp. 04001
Author(s):  
Sekar Sari ◽  
Indah Soesanti ◽  
Noor Akhmad Setiawan
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
False Positive ◽  
Lung Nodule ◽  
Cad Systems ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Lung Cancer Detection

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

scholarly journals Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists

2021 ◽  
Vol 13 (5) ◽  
pp. 2728-2737
Author(s):  
Alan A. Peters ◽  
Amanda Decasper ◽  
Jaro Munz ◽  
Jeremias Klaus ◽  
Laura I. Loebelenz ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Radiology Residents

scholarly journals Classification of lung nodules using CT images based on texture features and fractal dimension transformation

2019 ◽  
Vol 485 (5) ◽  
pp. 558-563
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomaryov ◽  
V. I. Pustovoit ◽  
E. Rendon-Gonzalez
Keyword(s):  
Fractal Dimension ◽  
Block Diagram ◽  
False Positive Rate ◽  
A Priori ◽  
Lung Nodule ◽  
Texture Features ◽  
Lung Nodules ◽  
Cad System ◽  
Positive Rate ◽  
Priori Information

A new computer-aided detection (CAD) system for lung nodule detection and selection in computed tomography scans is substantiated and implemented. The method consists of the following stages: preprocessing based on threshold and morphological filtration, the formation of suspicious regions of interest using a priori information, the detection of lung nodules by applying the fractal dimension transformation, the computation of informative texture features for identified lung nodules, and their classification by applying the SVM and AdaBoost algorithms. A physical interpretation of the proposed CAD system is given, and its block diagram is constructed. The simulation results based on the proposed CAD method demonstrate advantages of the new approach in terms of standard criteria, such as sensitivity and the false-positive rate.

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