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

Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.

Deep Learning* ; Breast Neoplasms*/diagnostic imaging ; Breast Neoplasms*/pathology; Humans ; Female ; Artificial Intelligence ; Machine Learning ; Mammography/methods ; Computers

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.
(© 2023. International Federation for Medical and Biological Engineering.)

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Keywords: Artificial intelligence; Breast cancer; CNN; Computer-aided diagnosis (CAD); Deep learning; FPGA

Date Created: 20230726 Date Completed: 20231023 Latest Revision: 20231214

20231215

10.1007/s11517-023-02883-2

37495885