What type of study is this?

This is a Quantitative Study study.

September 23, 2025

Pulmonary nodule detection model based on YOLOv8-MSDA

LSLiguo SongDalian Maritime University CCChuwen ChenSouth China Agricultural University MYMaolong YinNorthwestern Polytechnical University

Key Points

This model demonstrates a recall rate of 0.93, significantly improving detection accuracy for pulmonary nodules.
Achieving a mean Average Precision (mAP) of 89.7%, the model excels in identifying complex background nodules.
By integrating multi-scale feature fusion and an attention mechanism, the model enhances performance on the LIDC-IDRI dataset.
The method optimizes healthcare resource allocation for early lung cancer screening and effective patient management.

Abstract

To solve the problems of missed diagnoses and misdiagnoses caused by traditional methods' reliance on manual interpretation in pulmonary nodule detection, an improved YOLOv8 model integrating deep learning is provided in this paper. By implementing a cross-layer feature pyramid network for multi-scale feature fusion, integrating an MSDA attention mechanism to enhance the perception of pulmonary nodules, and adopting CLAME image enhancement technology to improve contrast, the model significantly improves detection accuracy and efficiency. Experimental results demonstrate that this method can effectively identify pulmonary nodules in lung CT images, achieving a recall rate of 0.93 and mAP of 89.7% on the LIDC-IDRI data set. This detection method can effectively identify small pulmonary nodules and those in complex backgrounds, providing a reliable tool for early lung cancer screening. There are significant implications for optimizing the allocation of healthcare resources.

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