FPGA Implementation of the Proposed DCNN Model for Detection of Tuberculosis and Pneumonia Using CXR Images

Abstract

The automated detection of tuberculosis (TB) and pneumonia (PN) from chest X-ray (CXR) images using artificial intelligence (AI) is challenging in clinical studies for rapid diagnosis and initiation of treatment. This letter proposes the field-programmable gate array (FPGA)-based hardware implementation of a novel lightweight deep convolutional neural network (DCNN) model to detect PN and TB ailments using CXR images. Initially, the proposed DCNN (consisting of ten layers) is trained using the Google Cloud central processing unit (CPU) to obtain the model weight and bias parameters. Then, the register transfer logic (RTL) for the trained DCNN model is generated by the VIVADO high-level synthesis (HLS) framework using HLS for machine learning (HLS4ML) with fixed-point representation (8 bit for integer and 12 bit for the fractional part). The hardware implementation of the suggested DCNN model is performed using the PYNQ-Z2 FPGA framework to detect TB and PN diseases automatically. The experimental results demonstrate that the proposed DCNN model has obtained accuracy values of 96.39% and 95.63% on the Google-Cloud CPU and PYNQ-Z2 FPGA frameworks using 422 CXR images in the inference phases. The inference time of the proposed DCNN model on the PYNQ-Z2 FPGA framework is reduced by 85.19% compared to the CPU-based implementation. The suggested DCNN model has only 1831 parameters, less than the transfer learning (TFL) and existing CNN-based models to detect TB and PN using CXR images.