scholarly journals Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

2021 ◽  
Author(s):  
Ahmet Saygılı
Keyword(s):  
Support Vector Machines ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Ct Images ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Vector Machines ◽  
Computer Aided ◽  
Kernel Support Vector Machines

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdullah Alharbi ◽  
Wajdi Alhakami ◽  
Sami Bourouis ◽  
Fatma Najar ◽  
Nizar Bouguila
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Support Vector ◽  
Learning Framework ◽  
Probabilistic Support ◽  
Discriminative Approach ◽  
Vector Machines

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

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.

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