FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement

TL;DR

This paper introduces FPAENet, a pneumonia detection network that enhances feature pyramid attention to improve detection accuracy in medical images, outperforming existing methods.

Contribution

The paper proposes a novel feature pyramid attention enhancement module with an attention mechanism to improve pneumonia lesion detection accuracy.

Findings

Achieves 4.02% higher detection performance than baselines.

Incorporates an attention mechanism for better feature integration.

Demonstrates significant improvement in pneumonia detection accuracy.

Abstract

Automatic pneumonia Detection based on deep learning has increasing clinical value. Although the existing Feature Pyramid Network (FPN) and its variants have already achieved some great successes, their detection accuracies for pneumonia lesions in medical images are still unsatisfactory. In this paper, we propose a pneumonia detection network based on feature pyramid attention enhancement, which integrates attended high-level semantic features with low-level information. We add another information extracting path equipped with feature enhancement modules, which are conducted with an attention mechanism. Experimental results show that our proposed method can achieve much better performances, as a higher value of 4.02% and 3.19%, than the baselines in detecting pneumonia lesions.