Lung Cancer Detection from Thoracic CT Scans Using 3-D Deformable Models Based on Statistical Anatomical Analysis

2011 ◽  
pp. 28-39
Author(s):  
Hotaka Takizawa ◽  
Shigeyuki Ishii
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Deformable Models ◽  
Ct Scans ◽  
Anatomical Analysis ◽  
Thoracic Ct ◽  
Lung Cancer Detection

scholarly journals Deep Learning for Lung Cancer Detection in Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists

2021 ◽  
Author(s):  
Colin Jacobs ◽  
Arnaud A. A. Setio ◽  
Ernst T. Scholten ◽  
Paul K. Gerke ◽  
Haimasree Bhattacharya ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Large Scale ◽  
Ct Scans ◽  
Observer Study ◽  
Lung Cancer Detection

scholarly journals Using Double Convolution Neural Network for Lung Cancer Stage Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Goran Jakimovski ◽  
Danco Davcev
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
Ct Scans ◽  
Cancer Stage ◽  
Extensive Training ◽  
Initial Dataset ◽  
Lung Cancer Detection

Recently, deep learning is used with convolutional Neural Networks for image classification and figure recognition. In our research, we used Computed Tomography (CT) scans to train a double convolutional Deep Neural Network (CDNN) and a regular CDNN. These topologies were tested against lung cancer images to determine the Tx cancer stage in which these topologies can detect the possibility of lung cancer. The first step was to pre-classify the CT images from the initial dataset so that the training of the CDNN could be focused. Next, we built the double Convolution deep Neural Network with max pooling to perform a more thorough search. Finally, we used CT scans of different Tx cancer stages of lung cancer to determine the Tx stage in which the CDNN would detect possibility of lung cancer. We tested the regular CDNN against our double CDNN. Using this algorithm, doctors will have additional help in early lung cancer detection and early treatment. After extensive training with 100 epochs, we obtained the highest accuracy of 0.9962, whereas the regular CDNN obtained only 0.876 accuracy.

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