Abstract
Coronary artery disease (CAD) is one of the leading causes of global mortality, necessitating accurate assessment of coronary artery stenosis and
plaque-associated angiographic findings
from coronary angiography images. Although deep learning-based diagnostic models have demonstrated high predictive capability, their deployment on resource-constrained embedded platforms remains challenging because of computational complexity and memory requirements. To address these limitations, this study proposes a knowledge-distillation-based hardware-aware framework that transfers discriminative knowledge from a high-capacity teacher network to a lightweight convolutional neural network (CNN). The proposed framework integrates software-level localization of
plaque-associated angiographic findings
with FPGA-oriented optimization. Using a 90% training and 10% testing split, the proposed model achieved an accuracy of
98.64%
, precision of
99.06%
, recall of
97.82%
, PPV of
0.99
, NPV of
0.98
, MCC of
0.96
, and an AUC of
0.9940
. And under 10-fold cross-validation, the model achieved a mean accuracy of
97.53%
±
0.27%
, precision of
97.82%
, recall of
96.94%
, PPV of
0.98
, NPV of
0.97
, MCC of
0.95
, and a mean AUC of
0.9897
, which confirms that our proposed model exhibits high robust performance. For hardware realization, the optimized student CNN was deployed on a
Xilinx Zynq UltraScale
+
MPSoC FPGA
using the
Vitis High-Level Synthesis (HLS)
toolchain. The implemented accelerator achieved a
kernel-level inference latency of 10.6 µs
and a
Peak On-Chip Kernel Throughput of approximately 94,339 inferences/s
, while maintaining balanced hardware resource utilization and low power consumption. Overall, the proposed framework demonstrates accurate classification of
plaque-associated angiographic findings
with efficient FPGA deployment.