Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination

2007 ◽  
Author(s):  
Saejoon Kim ◽  
Sejong Yoon ◽  
Donghyuk Shin
Keyword(s):  
Support Vector Machines ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

Multi-Class SVMs Based on Priority for Computer-Aided Diagnosis of Lung Nodules in CT Images

2011 ◽  
Vol 291-294 ◽  
pp. 2742-2745
Author(s):  
Qing Zhu Wang ◽  
Xin Zhu Wang ◽  
Ji Song Bie ◽  
Bin Wang
Keyword(s):  
Support Vector Machines ◽  
Sensitivity And Specificity ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Lung Nodules ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

A priority based ‘One against all (OAA)’ Multi-class Least Square-Support Vector Machines is designed to remove the unclassifiable regions exist in basic OAA. POAA develops the sensitivity and specificity in Computer-aided Diagnosis (CAD) for detection of lung nodules.

Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features

2013 ◽  
Vol 237 ◽  
pp. 59-72 ◽  
Author(s):  
J. Ramírez ◽  
J.M. Górriz ◽  
D. Salas-Gonzalez ◽  
A. Romero ◽  
M. López ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

Realization methods of computer-aided diagnosis system of medical images

2019 ◽  
Vol 13 ◽  
Author(s):  
Muhammad Aqeel Ashraf ◽  
Shahreen Kasim
Keyword(s):  
Data Model ◽  
Object Segmentation ◽  
Medical Images ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Cad System ◽  
Existing Problems ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

: In this paper, medical images are used to realize the computer-aided diagnosis (CAD) system which develops targeted solutions to existing problems. Relying on the Mi COM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows realization of diagnosis and treatment and sharing of data resources.

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Sign in / Sign up

Export Citation Format

Share Document