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A machine learning-based approach for the segmentation and classification of malignant cells in breast cytology images using gray level co-occurrence matrix (GLCM) and support vector machine (SVM)

  • S.I. : WorldCIST’20
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Abstract

Breast cancer is one of the common disease in female gender population all over the world. The classical methods of segmentation and classification for malignant cells are not only repetitive but also very time-consuming. Therefore, a computer-aided diagnosis is needed for automatic segmentation and classification of malignant cells in breast cytology images. In this article, a machine learning-based approach is proposed for malignant cell segmentation and classification in breast cytology images. In the proposed approach, the segmentation of cells is performed by a level set algorithm which is used to extract statistical information related to the malignant and benign cells. Similarly, the gray level co-occurrence matrix is computed to exploit the texture information, and support vector machine-based classification is used for the classification of malignant and benign cells. It has been observed through experiments that the proposed approach achieved high accuracy (96.3%) in the classification of malignant and benign cells.

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Authors and Affiliations

  1. Department of Computer Science, Islamia College University Peshawar, Peshawar, Pakistan

    Sana Ullah Khan, Naveed Islam, Zahoor Jan, Khalid Haseeb & Syed Inayat Ali Shah

  2. Department of Computer Science and Engineering, Ghulam, Ishaq Khan Institute of Engineering Sciences and Technology, Swabi, Pakistan

    Muhammad Hanif

Authors
  1. Sana Ullah Khan
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  2. Naveed Islam
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  3. Zahoor Jan
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  4. Khalid Haseeb
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  5. Syed Inayat Ali Shah
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  6. Muhammad Hanif
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Correspondence to Naveed Islam.

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Khan, S.U., Islam, N., Jan, Z. et al. A machine learning-based approach for the segmentation and classification of malignant cells in breast cytology images using gray level co-occurrence matrix (GLCM) and support vector machine (SVM). Neural Comput & Applic 34, 8365–8372 (2022). https://doi.org/10.1007/s00521-021-05697-1

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  • DOI: https://doi.org/10.1007/s00521-021-05697-1

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