Computer-assisted diagnosis for lung nodule detection using a neural network technique

1992 ◽  
Author(s):  
Shih-Chung B. Lo ◽  
Matthew T. Freedman ◽  
Jyh-Shyan Lin ◽  
Brian Krasner ◽  
Seong K. Mun
Keyword(s):  
Neural Network ◽  
Lung Nodule ◽  
Computer Assisted ◽  
Lung Nodule Detection ◽  
Computer Assisted Diagnosis ◽  
Nodule Detection ◽  
Assisted Diagnosis

scholarly journals A robust convolutional neural network for lung nodule detection in the presence of foreign bodies

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Schultheiss ◽  
Sebastian A. Schober ◽  
Marie Lodde ◽  
Jannis Bodden ◽  
Juliane Aichele ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Foreign Bodies ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

2018 ◽  
Vol 9 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Extraction Methods ◽  
Learning Technologies ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Histological Images ◽  
Assisted Diagnosis ◽  
The Cost

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

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