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Open AccessArticle

SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems

1
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
2
School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India
3
Department of Medical Imaging, Buddhist Dalin Tzu Chi General Hospital, Chiayi 622, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 432; https://doi.org/10.3390/s20020432
Received: 29 October 2019 / Revised: 8 January 2020 / Accepted: 10 January 2020 / Published: 12 January 2020
In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVM-enabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the Sorensen-Dice coefficient, t-test, and Pearson’s R measure. It is apparent from the results of the SVM-enabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters. View Full-Text
Keywords: Sorensen-Dice coefficient; t-test; Pearson’s R measure; ultrasound sensing systems; SVM-enabled intelligent genetic algorithmic model; breast cyst imagery Sorensen-Dice coefficient; t-test; Pearson’s R measure; ultrasound sensing systems; SVM-enabled intelligent genetic algorithmic model; breast cyst imagery
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Chang, C.-Y.; Srinivasan, K.; Chen, M.-C.; Chen, S.-J. SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems. Sensors 2020, 20, 432.

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