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Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images

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Abstract

Cancer is an illness that instils fear in many individuals throughout the world due to its lethal nature. However, in most situations, cancer may be cured if detected early and treated properly. Computer-aided diagnosis is gaining traction because it may be used as an initial screening test for many illnesses, including cancer. Deep learning (DL) is a CAD-based artificial intelligence (AI) powered approach which attempts to mimic the cognitive process of the human brain. Various DL algorithms have been applied for breast cancer diagnosis and have obtained adequate accuracy due to the DL technology’s high feature learning capabilities. However, when it comes to real-time application, deep neural networks (NN) have a high computational complexity in terms of power, speed, and resource usage. With this in mind, this work proposes a miniaturised NN to reduce the number of parameters and computational complexity for hardware deployment. The quantised NN is then accelerated using field-programmable gate arrays (FPGAs) to increase detection speed and minimise power consumption while guaranteeing high accuracy, thus providing a new avenue in assisting radiologists in breast cancer diagnosis using digital mammograms. When evaluated on benchmark datasets such as DDSM, MIAS, and INbreast, the suggested method achieves high classification rates. The proposed model achieved an accuracy of 99.38% on the combined dataset.

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Funding

This work was funded by Xilinx Women In Technology Fall Grant 2021.

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Authors and Affiliations

  1. Department of Electronics and Communication, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Kayalvizhi R, Heartlin Maria H & Malarvizhi S

  2. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Revathi Venkatraman

  3. Department of Translational Medicine and Research, SRM Medical College Hospital and Research Centre, Kattankulathur, Chennai, 60320, India

    Shantanu Patil

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  1. Kayalvizhi R
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Correspondence to Malarvizhi S.

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R, K., H, H.M., S, M. et al. Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images. Med Biol Eng Comput 61, 2843–2857 (2023). https://doi.org/10.1007/s11517-023-02883-2

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