Abstract
Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.
Highlights
Pulmonary nodules [1, 2] are the result of the competition between unknown antigens and the body’s cellular and humoral immune functions
It is very harmful to the human body. It is an early manifestation of lung cancer [3]
The lung tissue is complex, and it is difficult to distinguish lung nodules from blood vessels and bronchus in chest tissue very accurately based on the experience of clinicians and film readers
Summary
Pulmonary nodules [1, 2] are the result of the competition between unknown antigens and the body’s cellular and humoral immune functions. The YOLO-V3 target detection network in deep learning is selected and the network is improved.
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