Pulmonary Nodule Detection Based on Multiscale Feature Fusion

Abstract

As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis.

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

“…It extracts the area of interest and location of the nodule. This increases the accuracy of medical data and faster the result to detect nodule detection based on multiscale feature fusion [6]. The significance of chest CT scans in the process of diagnosing lung cancer and correcting anomalies discovered in chest radiography [7].…”

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

“…Pulmonary nodules (PNs) are non-transparent lesions surrounded by the lung parenchyma that are not attributable to pleural effusion, atelectasis, or mediastinal lymphadenopathy [1][2][3]. These nodules include solid PNs (SPNs) as well as subsolid PNs, with different nodule types necessitating different management strategies as per the Fleischner guidelines [4].…”

Association between non-alcoholic fatty liver disease and the risk of pulmonary nodules in patients with intestinal polyps

“…Important characteristics of lung cancer, compared with other types of cancer, include a rapid onset period, a higher malignancy, and a more difficult diagnosis of the disease in its early stages ( 14 ). Due to developments in and the increasing utility of computer tomography (CT) in most countries, the detection of pulmonary nodules is now the most important early indicator of lung cancer ( 15 , 16 ). It is well documented that smoking and genetic risk factors are risk factors of lung cancer ( 17 , 18 ).…”