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CNN-SVM with Data Augmentation for Robust Blur Detection of Digital Breast Tomosynthesis Images

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Intelligent Multimedia Signal Processing for Smart Ecosystems

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Abstract

Digital breast tomosynthesis (DBT) is a method that extends digital mammography by capturing pictures of the breast from various angles of the x-ray source. DBT’s angular sampling range is severely limited due to hardware constraints, resulting in severely limited angular artefacts such as blurring and low contrast effects in the reconstructed images. Unwanted artefacts like blurry artefacts can substantially obscure the cancer site, particularly in exceptionally thick fibro glandular breast tissue, and reduce diagnostic accuracy. Due to the blurry artefact problem, it is essential to develop methods for analyzing the blur distortion of DBT-obtained images for diagnostic reasons. This chapter describes a hybrid convolutional neural network-support vector machine (CNN-SVM) strategy extracting robust hierarchical features from images using CNN before passing the images to an SVM classifier for classifier boosting to categorize DBT images into two classes: blur or non-blur images. To make the prediction invariance of image scaling and rotation more robust, a variety of data augmentation strategies are examined. The suggested tool was evaluated using the metrics of overall accuracy, recall, precision and processing time. The findings demonstrate that the combined CNN and SVM model outperforms standard feature models with an accuracy of 0.97 and an area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.9998, as well as numerous classical deep CNN models.

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Acknowledgements

This research work is financially supported by the Ministry of Higher Education Grant Scheme (FRGS) “A New Cascaded Convolutional Neural Network Model for Deblurring and Contrast Enhancement of Extremely Dense Breast Tissue in Digital Breast Tomosynthesis Images” (Ref: FRGS/1/2021/TK0/UiTM/02/19). The Advanced Control System and Computing Research Group (ACSCRG), Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Integrative Pharmacogenomics Institute (iPROMISE), and Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang are among the groups for which the authors are grateful for their support and direction during the fieldwork. Finally, the authors thank Universiti Teknologi MARA, Cawangan Pulau Pinang, Malaysia, for their immense administrative and financial support.

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Authors and Affiliations

  1. Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Penang, Malaysia

    Nur Athiqah Harron, Siti Noraini Sulaiman, Muhammad Khusairi Osman & Iza Sazanita Isa

  2. Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Penang, Malaysia

    Siti Noraini Sulaiman

  3. Integrative Pharmacogenomics Institute (iPROMISE), UiTM Puncak Alam Campus, Puncak Alam, Selangor, Malaysia

    Siti Noraini Sulaiman

  4. Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

  5. Breast Cancer Translational Research Programme (BCTRP), Advanced Medical & Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

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  1. Nur Athiqah Harron
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  2. Siti Noraini Sulaiman
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  3. Muhammad Khusairi Osman
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  4. Noor Khairiah A. Karim
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  5. Iza Sazanita Isa
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Correspondence to Siti Noraini Sulaiman .

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Editors and Affiliations

  1. Department of Electronics and Instrumentation, University of Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir A. Parah

  2. Aligarh Muslim University, Aligarh, India

    Nasir N. Hurrah

  3. Department of Electronics Engineering, Aligarh Muslim University, AMU Aligarh, India

    Ekram Khan

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Harron, N.A., Sulaiman, S.N., Osman, M.K., Karim, N.K.A., Isa, I.S. (2023). CNN-SVM with Data Augmentation for Robust Blur Detection of Digital Breast Tomosynthesis Images. In: Parah, S.A., Hurrah, N.N., Khan, E. (eds) Intelligent Multimedia Signal Processing for Smart Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-34873-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-34873-0_6

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