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All experimental cases were randomly divided into five groups, and one group was chosen as a testing group by turns while the remaining four groups were used to train the support vector machine with Genetic algorithms

Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images.

Computerized Medical Imaging and Graphics, no. 8 (2012): 627-633

Cited: 54|Views2103
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Abstract

To promote the classification accuracy and decrease the time of extracting features and finding (near) optimal classification model of an ultrasound breast tumor image computer-aided diagnosis system, we propose an approach which simultaneously combines feature selection and parameter setting in this study.

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Introduction
  • Breast cancer has been the main cause of death for women globally in recent years. According to statistics, in 2009, 192,370 women in the United States were expected to be diagnosed with breast cancer, and 40,170 deaths were attributed to the disease [1].
  • Detection of breast cancer usually consists of a physical examination, imaging, and biopsy [2].
  • Biopsy is the best way to accurately determine whether the tumor is benign or malignant.
  • It is invasive and is much more expensive than other detection methodologies.
Highlights
  • Breast cancer has been the main cause of death for women globally in recent years
  • All experimental cases were randomly divided into five groups, and one group was chosen as a testing group by turns while the remaining four groups were used to train the support vector machine with Genetic algorithms
  • We evaluated the ultrasound breast tumor images with the new six-dimensional feature vector
  • We considered evaluating breast tumor images using all 30 features
  • The feature selection step is implemented with the genetic algorithm and identifies the significant features used to evaluate the breast tumor images
  • Parameters setting for a support vector machine are often designed quite differently due to the unique characteristics of the data
Methods
  • A total of 210 ultrasound images that had been pathologically proven were used to evaluate the CAD system.
  • The ultrasound image database included 120 benign breast tumor images and 90 malignant ones.
  • The patients’ ages ranged from 18 to 64 years old and only one image from each patient is contained in the database.
  • This study was approved by the local ethics committee and informed consent was waived.
  • The patient was in supine position with arms extended overhead.
  • No acoustic standoff pad was used with any of the cases
Results
  • This study used the 5-fold cross-validation proposed by Salzberg [39]. That is, all experimental cases were randomly divided into five groups, and one group was chosen as a testing group by turns while the remaining four groups were used to train the SVM with GA.

    The proposed approach was implemented on a PC with an AMD Athlon 64 1.8 GHz processor, Windows XP operating system, and the Visual C++ 6.0 development environment.
  • The libSVM [40] is used in the proposed approach.
  • For SVM with GA, the authors set the following parameter values: population size is 20, crossover rate is 90%, mutation rate is 10%, the maximum number of solutions evaluated is 10,000 for GA, while the range of parameter C is 1–30,000 and the range of parameter is 0.001–1 for the SVM.
  • Each component f[i] of the feature vector was defined as follows:
Conclusion
  • The authors proposed an automatic diagnostic system that uses practical texture and morphological features to effectively distinguish between benign and malignant lesions of the breast in this study.
  • The authors considered evaluating breast tumor images using all 30 features.
  • The feature selection step is implemented with the genetic algorithm and identifies the significant features used to evaluate the breast tumor images.
  • Trial-and-error method seems to be the most common way to identify the nearoptimal parameters of an SVM.
  • It is time-consuming and does not guarantee the better result.
  • The authors use the GA to find the near-optimal parameters C and of the SVM
Tables
  • Table1: Features selected among five runs
  • Table2: Objectively indices result of the different approaches/features
  • Table3: Computational time required for various approaches to obtain their best models (time of feature extraction is not included)
  • Table4: Paired t-tests on average classification accuracy for various approaches
  • Table5: Time comparison of calculating features of all breast tumor images with different approaches
Download tables as Excel
Funding
  • This research was partially supported by the National Science Council of the Republic of China (Taiwan) under Contract no
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