Multiscale CNN with compound fusions for false positive reduction in lung nodule detection

2021 ◽  
Vol 113 ◽  
pp. 102017
Author(s):  
Pardha Saradhi Mittapalli ◽  
Thanikaiselvan V
Keyword(s):  
False Positive ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
False Positive Reduction ◽  
Nodule Detection

scholarly journals Computerized detection of lung nodules with an enhanced false positive reduction scheme

2021 ◽  
Author(s):  
Negar Memarian
Keyword(s):  
False Positive ◽  
State Of The Art ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Hybrid Scheme ◽  
Rule Based ◽  
Lung Nodule Detection ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Interest Detection

This thesis is based on the original investigations of the author in the field of computerized lung nodule detection in computed tomography (CT) images. The methodologies discussed in this thesis include two main topics: region of interest detection and enhanced false positive (FP) reduction. The system, which is developed to be a supplementary diagnostic tool for radiologists, first spots all the regions suspected to be nodules in the lung. Then it pins down the candidates with the highest possibility of being nodules through a series of rule based filtering stages. Finally, an enhanced false positive reduction system, which is in fact designed as a hybrid scheme based on learning algorithms, reduces the false positive detections further. The overall system performs with 72% sensitivity and 2.42 FP/slice, which competes with state-of-the-art methods. The system was tested on a database consisting of 24 pediatric clinical subjects with 1190 images and 154 metastatic nodules.

scholarly journals Computerized detection of lung nodules with an enhanced false positive reduction scheme

2021 ◽  
Author(s):  
Negar Memarian
Keyword(s):  
False Positive ◽  
State Of The Art ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Hybrid Scheme ◽  
Rule Based ◽  
Lung Nodule Detection ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Interest Detection

This thesis is based on the original investigations of the author in the field of computerized lung nodule detection in computed tomography (CT) images. The methodologies discussed in this thesis include two main topics: region of interest detection and enhanced false positive (FP) reduction. The system, which is developed to be a supplementary diagnostic tool for radiologists, first spots all the regions suspected to be nodules in the lung. Then it pins down the candidates with the highest possibility of being nodules through a series of rule based filtering stages. Finally, an enhanced false positive reduction system, which is in fact designed as a hybrid scheme based on learning algorithms, reduces the false positive detections further. The overall system performs with 72% sensitivity and 2.42 FP/slice, which competes with state-of-the-art methods. The system was tested on a database consisting of 24 pediatric clinical subjects with 1190 images and 154 metastatic nodules.

scholarly journals Multi-Branch Ensemble Learning Architecture Based on 3D CNN for False Positive Reduction in Lung Nodule Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67380-67391 ◽  
Author(s):  
Haichao Cao ◽  
Hong Liu ◽  
Enmin Song ◽  
Guangzhi Ma ◽  
Xiangyang Xu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
False Positive ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
3D Cnn

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 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.

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