Computerized lung nodule detection on thoracic CT images: combined rule-based and statistical classifier for false-positive reduction

2001 ◽  
Author(s):  
Metin N. Gurcan ◽  
Nicholas Petrick ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Philip N. Cascade ◽  
...  
Keyword(s):  
False Positive ◽  
Lung Nodule ◽  
Ct Images ◽  
Rule Based ◽  
Statistical Classifier ◽  
Lung Nodule Detection ◽  
Thoracic Ct ◽  
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
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