Support Vector Machines for Diagnosis of Breast Tumors on US Images

Chang, Ruey-Feng; Wu, Wen; Moon, Wookyung; Chou, Yi‐Hong; Chen, Dar Ren

doi:10.1016/s1076-6332(03)80044-2

Cited by 101 publications

(58 citation statements)

References 22 publications

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“…Since different tissues have different textures, textural features provide useful information for classifying breast tumors as benign or malignant. The autocovariance matrix can specify the inter-pixel relationships in an image and it has been used to classify breast tumors [8,21,22]. In the method that was proposed by Chang et al [22], the modified auto-covariance coefficients inside a tumor are defined as follows.…”

Section: Textural Featuresmentioning

confidence: 99%

“…Kuo et al [19] combined the AIS algorithm with a fuzzy neural network (FNN) to increase the accuracy of an RFID-based positioning system. This work develops an efficient SVM [20][21][22][23][24][25] method that is based on the AIS algorithm (AISSVM) for diagnosing ultrasound images of breast tumors. The proposed CAD system simultaneously performs parameter tuning and feature selection, and thus achieves high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

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A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96. 67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

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Section: Textural Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

Self Cite

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“…There were a number of motivations for selecting SVMs as a classification mechanism. SVMs have been shown to perform well in medical diagnosis applications [16], and have also been shown to perform well when dealing with relatively small training sets [17]. This was particularly appealing given the inherent difficulty in acquiring large amounts of screening data devoted exclusively to training.…”

Section: Discussionmentioning

confidence: 99%

“…Support vector machines (SVMs) have been shown to perform well as a computer-aided diagnostic classification mechanism for breast cancer screening in ultrasound [16] and mammography [17]. More recently it has been shown that SVMs outperform a variety of other machine learning techniques when applied to the separation of malignant and benign DCE-MR breast lesions [18]- [19].…”

Section: Introductionmentioning

confidence: 99%

Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines

Levman

Leung

Causer

et al. 2008

IEEE Trans. Med. Imaging

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Early detection of breast cancer is one of the most important factors in determining prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be the most sensitive modality for screening high-risk women. Computeraided diagnosis (CAD) systems have the potential to assist radiologists in the early detection of cancer. A key component of the development of such a CAD system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The purpose of this study is to evaluate the effects of variations in temporal feature vectors and kernel functions on the separation of malignant and benign DCE-MRI breast lesions by support vector machines. We also propose and demonstrate a classifier visualization and evaluation technique. We show that support vector machines provide an effective and flexible framework from which to base computer-aided diagnosis techniques for breast MR imaging, and that the proposed classifier visualization technique has potential as a mechanism for the evaluation of classification solutions.

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“…SVMs have already been applied to breast cancer detection methods, giving rise to very good results. In a couple of cases the SVM was used for reducing false-positive signals, in the detection of microcalcifications in mammograms (Bazzani et al 2001), and in the diagnosis of breast ultrasonography images (Chang et al 2003): in both cases SVM classified signals by means of extracted image features. A featureless approach based on SVM for the detection of lesions in mammograms has been investigated for the first time by our group (Campanini et al 2002).…”

Section: Introductionmentioning

confidence: 99%

A novel featureless approach to mass detection in digital mammograms based on support vector machines

et al. 2004

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Abstract. In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the masses appearance is the main obstacle of building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; on the contrary, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first SVM classifier. The detection task is here considered as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.

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Support Vector Machines for Diagnosis of Breast Tumors on US Images

Cited by 101 publications

References 22 publications

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines

A novel featureless approach to mass detection in digital mammograms based on support vector machines

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