Real-time deployment of BI-RADS breast cancer classifier using deep-learning and FPGA techniques

Breast cancer is commonly recognized as the second most frequent malignancy in women worldwide. Breast cancer therapy includes surgical surgery, radiation therapy, and medication which can be exceedingly successful, with 90% or higher survival rates, especially when the condition is discovered early. This work is one such approach for early detection of breast cancer relying on the BI-RADS score. In this regard, a computer-aided-diagnosis system based on a bespoke Digital Mammogram Diagnostic Convolutional Neural Network (DMD-CNN) model that can aid in the categorization of mammogram breast lesions is proposed. Furthermore, a PYNQ-based acceleration through the Artix 7 FPGA is employed for deployment of DMD-CNN model's hardware acceleration platform which is the first of its kind for breast cancer, yielding a performance accuracy of 98.2%, the proposed model exceeded the state-of-the-art approach. The comparative analysis performed in the study has shown that the proposed method has resulted in a 4% increase in accuracy and a good recognition rate of 96% when compared to the existing model. A k-fold cross-validation (k = 5, 7, 9 the reported accuracy score values are 96.2%, 97.5% and 98.1%, respectively) approach was used to test and assess the integrated system. Extensive testing using mammography datasets was carried out to determine the increased performance of the suggested approach. Experiments reveal that when compared to the DMD-CNN model acceleration to GPU, the suggested solution not only optimizes resource utilization but also decreases power consumption to 3.12 W. Hardware acceleration through FPGA resulted in processing and analyzing nearly 91 images in a second where a single image will be processed using CPU.