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Evaluating the Efficacy of Gabor Features in the Discrimination of Breast Density Patterns Using Various Classifiers

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Classification in BioApps

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 26))

Abstract

A prevalent predictor of breast cancer is high density of the breast tissue. The characterization of density patterns of the breast is of clinical significance because, as has been stated frequently, the possibility of developing breast cancer is increased if the breast tissue is of high density. Radiologists predict breast tissue density by visually examining the mammogram, and the accuracy of this diagnosis is solely dependent on the experience of the radiologist. Moreover, a proper differentiation between the atypical cases of density patterns (with highly overlapping density patterns on the mammogram) is a formidable task, even for an experienced radiologist. Therefore, in the current work, different experiments were performed with a view to designing efficient computer aided diagnostic (CAD) models with which to characterize the density of breast tissue. To implement the proposed algorithms, mammographic images were taken from the mini-MIAS (Mammographic Image Analysis Society) dataset. From each mammographic image, a region of interest (ROI) (200 × 200 pixels in size) was cropped from the central part of the breast tissue. From the extracted ROI, texture information was computed in the form of mathematical descriptors using a 2D Gabor wavelet transform (GWT). The performance of GWT features for the characterization of the density patterns of the breast tissue was evaluated using different classifiers. For both 2-class and 3-class classification, neural network (NN)-based CAD models yielded better results compared with other classifier-based CAD models. The classification accuracy achieved for 2-class classification was 93.7% and for 3-class classification the classification accuracy was 87.5%.

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Acknowledgements

The authors zealously acknowledge the supportive and bettering comments and suggestions of the reviewers, which have lead to the improvement of the present work.

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Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, Punjab, India

    Kriti & Ravinder Agarwal

  2. Central Scientific Instruments Organization, Chandigarh, India

    Jitendra Virmani

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  1. Kriti
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  2. Jitendra Virmani
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  3. Ravinder Agarwal
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Correspondence to Kriti .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  2. Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. K.S. Institute of Technology, Bangalore, Karnataka, India

    Surekha Borra

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Kriti, Virmani, J., Agarwal, R. (2018). Evaluating the Efficacy of Gabor Features in the Discrimination of Breast Density Patterns Using Various Classifiers. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-65981-7_5

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  • Online ISBN: 978-3-319-65981-7

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