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Classification of Benign and Malignant Breast Lesions in Ultrasound Images using Support Vector Machines
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| Author:
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TELAGARAPU PRABHAKAR, ANNAPANTULA SUDHAKAR
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| Abstract:
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One of the most common diseases prevalent among women is Breast cancer. In general, Mammography is applied for the detection of the breast cancer, whereas, the technique of Mammography makes use of the ionizing radiation. As a result, this technique does not detect the breast cancer in women having dense breast. In the beginning phases of malignancy, radiologist could experience the challenges in identifying the lesions. Hence, there is a need for one more screen test for the confirmation of the cancer. On the other hand, Sonography is also commonly used for the detection and classification of abnormalities of the breast owing to its cost-effectiveness, comfort level of patient, and non-ionizing radiation. Nevertheless, ultrasound images seem to be low contrast images as well as the physician could encounter the possible challenges in the identification of the lesions. Therefore, there has been an inevitable requirement in the development of Computer Aided Diagnosis (CAD) system for the purpose of classifying breast lesion in an accurate manner. This paper aims at the analyzing the Morphological and Fractal features in an effective manner for differentiating benign as well as malignant masses from ultrasound images. .
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| Keyword:
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Breast Ultrasound, Morphological features, Fractal features, Support Vector Machine (SVM), Fuzzy K-Nearest Neighbor (FKNN)
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.03.025
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| Download:
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