A novel method based on a multiscale convolution neural network for identifying lung nodules.
Authors
Affiliations (10)
Affiliations (10)
- Collaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China. [email protected].
- Faculty of Medicine, University of Banja Luka, Banja Luka, 78000, Bosnia and Herzegovina. [email protected].
- Collaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China.
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
- Collaborative Innovation Center for Biomedicine, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China. [email protected].
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
- Business School, University of Shanghai for science and technology, Shanghai, 200093, China.
- Chongming Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, 202150, China.
- Antai College Economics and Management, Shanghai Jiao Tong University, Shanghai, 200030, China.
Abstract
Deep learning techniques in image processing are gaining widespread application, with growing research in medical image analysis and diagnosis driven by advancements in image recognition technology. This study aimed to addresses the challenges of lung nodule recognition and classification using convolutional neural networks (CNN) and proposes a novel multiscale convolutional neural network (MCNN) model. The MCNN model integrates Gaussian Pyramid Decomposition (GPD) to enhance CNN-based image recognition for lung nodule detection. A practical study was conducted to apply the MCNN model, and its performance was compared with various algorithmic models and classifiers. Experimental results show that the MCNN model outperforms traditional CNN methods, particularly in detecting solid nodules and pure ground-glass nodules, with an improvement in F1 values of over 2.0%. Furthermore, the MCNN model demonstrated superior overall accuracy in lung nodule detection. These findings underline the practical implications of deep learning in advancing medical image analysis and diagnosis, offering new possibilities for improving the prognosis of lung nodule-relate diseases.