Feature-aware attention convolutional neural network for efficient pulmonary fibrosis classification from chest CT images with FPGA deployment.

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Abstract

Pulmonary Fibrosis (PF) is a progressive interstitial lung disease that requires accurate analysis of chest Computed Tomography (CT) images, while conventional image interpretation is time-consuming and dependent on expert assessment. Although deep learning has demonstrated strong performance for automated PF classification, many existing approaches are primarily designed for software-based execution and do not explicitly consider deployment on resource-constrained embedded hardware. To address this limitation, this study proposes a lightweight Feature-Aware Attention Convolutional Neural Network (FAA-CNN) for binary classification of pulmonary fibrosis and normal CT images with FPGA-oriented design considerations. The proposed architecture integrates multi-level feature fusion and feature-wise attention gating to enhance discriminative feature representation while maintaining a compact computational structure. The trained model is translated into synthesizable VHDL using MATLAB HDL Coder and implemented for the PYNQ-Z2 FPGA with a Zynq-7020 device using Xilinx Vivado. Experimental evaluation on 5,120 CT images demonstrates that the proposed FAA-CNN achieves 97.82% accuracy, 97.18% sensitivity, and 98.46% specificity, outperforming the analytical baseline and base CNN. The FPGA implementation achieves timing closure at 47 MHz and utilizes 5,236 LUTs, 4,931 flip-flops, 106.5 BRAM tiles, and 10 DSP48E1 slices. Vivado estimates a total on-chip power of 0.139 W, comprising 0.028 W dynamic and 0.112 W device static power. These results demonstrate the feasibility of combining pulmonary fibrosis image classification with FPGA-oriented implementation.

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