YOLOv8-BCD: a real-time deep learning framework for pulmonary nodule detection in computed tomography imaging.

ABSTRACT:

Background

Lung cancer remains one of the malignant tumors with the highest global morbidity and mortality rates. Detecting pulmonary nodules in computed tomography (CT) images is essential for early lung cancer screening. However, traditional detection methods often suffer from low accuracy and efficiency, limiting their clinical effectiveness. This study aims to devise an advanced deep-learning framework capable of achieving high-precision, rapid identification of pulmonary nodules in CT imaging, thereby facilitating earlier and more accurate diagnosis of lung cancer.

Methods

To address these issues, this paper proposes an improved deep-learning framework named YOLOv8-BCD, based on YOLOv8 and integrating the BiFormer attention mechanism, Content-Aware ReAssembly of Feature

SUBMITTER: Zhu W 

PROVIDER: S-EPMC12397618 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature