Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network.

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

Heavy metals in soils from a typical industrial area in Sichuan, China: spatial distribution, source identification, and ecological risk assessment.

Anthropogenic activities could result in increasing concentrations of heavy metals in soil and deteriorating in soil environmental quality. Topsoil samples from a typical industrial area, Shiting River Valley, Sichuan, Southwest China, were collected and determined for the concentrations of Cu, Zn, Cr, Cd, As, and Hg. The mean concentrations of these metals were lower than the national threshold values, but were slightly higher than their corresponding background values, indicating enrichment of these metals in soils in the valley, especially for Cu, Zn, and Hg. The topsoils in this area demonstrated moderate pollution and low potential ecological risk. Principal component analysis coupled with cluster analysis was applied to analyze the data and identified possible sources of these heavy metals; the results showed that soil Cd, Hg, As, Cu, and Zn were predominantly controlled by human activities, whereas Cr was mainly from the parent material. The spatial distribution of the heavy metals varied distinctly and was closely correlated to local anthropogenic activities. Furthermore, the concentrations of heavy metals in the industrial land demonstrated relatively higher levels than those of other land use patterns. Soil metal concentrations decreased with the distance increase from the traffic highway (0-1.0 km) and water system (0-2.0 km). Additionally, soil properties, especially pH and soil organic matter, were found to be important factors in the distribution and composition of metals.