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

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-basedartificial 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 haveobtained 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 aminiaturised 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, thusproviding 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.