To make full use of the pulmonary nodules features extracted by convolutional network and improve detection of pulmonary nodules, an improved Mask R-CNN pulmonary nodules lesion detection algorithm fused with attention mechanism was proposed. The improved multi-scale pyramid is used to enhance the contextual lung nodules information, so that network can obtain high-level semantic features without losing underlying texture information. In order to obtain richer pulmonary nodules lesion features, residual network fused with attention model is designed to extract nodules channel and spatial information. Experiments were carried out on the lung nodules analysis 16 (LUNA16) dataset. The improved algorithm achieved an average detection sensitivity of 96.8% and an average segmentation Dice coefficient of 96.2% for lung nodules lesion detection in the dataset. The results show that proposed algorithm can effectively detect and segment lung nodules lesions.
Doping silicon with chalcogens (S, Se, Te) via femtosecond-laser irradiation lead to increase the absorptance of Si in both visible and infrared region, so chalcogens doped silicon have great potential for use in Si-based optoelectronic devices. Tellurium doped silicon was fabricated by femtosecond-laser irradiation of Si with Si/Te bilayer films. The influence of distance between the sample surface and the laser focus in the process of fabricating micro-structured Si was studied. The results show that the sample surface cannot be located in focal plane, nor is far from the focal plane, suitable distance is necessary to produce regular columnar structure. And the surface structure of doped silicon is vitally important to high absorptance. In addition, we report the dependence of surface morphology and optical properties on scanning speed. The absorptance increases over the entire wavelength as the scanning speed decreases.
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