Feature-Based Classification of Prostate Ultrasound Images using Multiwavelet and Kernel Support Vector Machines

Zaim, Amjad; Yi, Taeil; Keck, Rick W.

doi:10.1109/ijcnn.2007.4370968

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…Transformation-based approaches, such as the Gabor filter response [66][67][68][69] and wavelet [70][71][72] methods, represent an image in a space in which the coordinate system can be interpreted in a manner that is related to the characteristics of the texture, such as the frequency [73].…”

Section: Appearancementioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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Section: Appearancementioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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show abstract

“…SVMs are basically used for regression and classification functions, which are called "classifying SVM", support vector regression (SVR), respectively. Support vector machines have been applied successfully in many problems such as speech recognition (Changxue et al, 2001), signal recognition (Gexiang et al, 2004), text categorization (Pan et al, 2009), gene selection (Zhang Q, 2007), intrusion detection (Zhenguo and Guanghua, 2009), spam filtering (Amayri and Bouguila, 2009), forecasting (Shen et al, 2006;Guo-Rui et al, 2007;Liu et al, 2009;Shu-xi and Wang, 2006;Tian et al, 2009), medical image classification (Bai and Tian, 2009;Zaim et al, 2007), classification (Changsheng et al, 2003;Jing et al, 2009;Zai-Wen et al, 2009;Reljin and Pokrajac, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey

Timor¹

2012

Afr. J. Bus. Manage.

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Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that find a wide range of applications both in the field of engineering and social sciences. Recently, studies especially in the field of finance for the classification and estimation make it necessary to use these methods often in this area. In this study, different SVM and ANN models for the problem of stocks selection which provide maximum returns have been applied on different combinations of data sets which obtained from the balance sheets, stocks prices and the results of a comparative analysis has been presented. The findings show that SVM and ANN models including financial ratios give meaningful performance results for the stock selection.

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“…Fatty liver disease relates to the category of diffused liver diseases that is caused due to the enormous deposit of triglycerides and other fat types in the liver cells. Inspite of the potential characteristics of ultrasonic images, the activity involved in classifying the normal cells from infected cells of the liver is influenced by minimum contrast, close appearances and hazy nature of images [5][6][7]. Inherently, the ultrasonic image of one liver disease may closely resemble the image of other liver disease or the similar ultrasonic images of the same liver disorder may exhibit different textures.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

Rajesh¹

2018

CVRJST

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In the treatment of abdominal diseases like fatty liver disorder, ultrasonic images-based investigation is considered as the primary step of diagnosis. But, the noisy feature of ultrasonic images combined with the least contrasting features introduces maximum complexity during the process of automated classification. This paper contributes an Improved Active Contour Segmentation scheme for effective segmentation. Then Gray Level Co-occurrence Matrix (GLCM) and fractal features are extracted from the segmented ultrasonic images and the classification is achieved using an enhanced forest classification incorporated in the artificial neural networks (ANN) for accurate detection. The results of the proposed automated approach are investigated using classification accuracy, mean classification accuracy, true positive rate, false positive rate and true negative rate. The results of the proposed scheme also infers a classification accuracy rate of 98.73% and a mean classification accuracy rate of 97.65% compared to the baseline automated liver disorder classification techniques.

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Feature-Based Classification of Prostate Ultrasound Images using Multiwavelet and Kernel Support Vector Machines

Cited by 15 publications

References 8 publications

A survey of prostate modeling for image analysis

A survey of prostate modeling for image analysis

Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

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