FPGA-Based ResNet-50 Acceleration for Automated Pneumonia Detection from Chest X-Ray Images

DOI

https://www.doi.org/10.59256/indjece.20260302005

Published In

Year 2026 · Volume 3 · Issue 2

Published Online

May-August 2026

Pages

40-46

Article Type

Original Article

Check for updates

Pneumonia remains a leading cause of morbidity and mortality worldwide, and timely radiological screening is essential for effective treatment, particularly in settings where specialist radiologists are scarce. Convolutional neural networks (CNNs) classify chest X-ray images as normal or pneumonic with high accuracy, but their computational cost limits deployment on portable, low-power clinical devices. This paper presents an end-to-end pipeline that trains a ResNet-50 network, using transfer learning, to classify chest radiographs and deploys the trained model on a Xilinx ZCU104 Zynq UltraScale+ FPGA board through the Vitis AI toolchain. Images from a public pediatric pneumonia dataset (5216 training, 624 test, 16 validation images) are resized, normalized, and evaluated under several classical filtering operations before being passed to the network, which combines residual convolutional blocks, ReLU activations, and a sigmoid output layer. The model is trained for 30 epochs with the Adam optimizer and binary cross-entropy loss, reaching a validation accuracy in the 90–92% range. After quantization and compilation for the on-board Deep-Learning Processing Unit, the FPGA implementation achieves an inference throughput of 1250 frames per second at 4.1 W, compared with 44 frames per second at 19.9 W on a general-purpose CPU baseline — approximately a 28-fold gain in throughput and a 138-fold gain in energy efficiency. These results indicate that SoC-FPGA platforms are a practical route to real-time, energy-efficient pneumonia screening at the point of care.