Automatic Pulmonary Nodule Detection Using Faster R-CNN Based on Densely Connected Network

Yu, Shangqian; Wang, Yulin; Liu, Qian

doi:10.1145/3570773.3570814

Cited by 2 publications

(1 citation statement)

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“…Their reported performance has reached a sensitivity of 0.945. Yu et al [11] proposed an improved 3D faster R-CNN architecture. The proposed network uses a densely connected network to achieve feature propagation and reduce the problem of gradient disappearance and reach a sensitivity of 0.952.…”

Section: Related Workmentioning

confidence: 99%

Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

Lee,

Hsu,

Chien

2024

Bioengineering

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A deep convolution network that expands on the architecture of the faster R-CNN network is proposed. The expansion includes adapting unsupervised classification with multiple backbone networks to improve the Region Proposal Network in order to improve accuracy and sensitivity in detecting minute changes in images. The efficiency of the proposed architecture is investigated by applying it to the detection of cancerous lung tumors in CT (computed tomography) images. This investigation used a total of 888 images from the LUNA16 dataset, which contains CT images of both cancerous and non-cancerous tumors of various sizes. These images are divided into 80% and 20%, which are used for training and testing, respectively. The result of the investigation through the experiment is that the proposed deep-learning architecture could achieve an accuracy rate of 95.32%, a precision rate of 94.63%, a specificity of 94.84%, and a high sensitivity of 96.23% using the LUNA16 images. The result shows an improvement compared to a reported accuracy of 93.6% from a previous study using the same dataset.

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Section: Related Workmentioning

confidence: 99%