Computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines

2015 ◽  
Vol 12 (2) ◽  
pp. 026008 ◽  
Author(s):  
Francisco P M Oliveira ◽  
Miguel Castelo-Branco
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Support Vector Machines ◽  
Computer Aided Diagnosis ◽  
Binding Potential ◽  
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.

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