Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

Background: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.

Materials And Methods: Open-source data sets and multicenter data sets have been used in this study.

[Variation characteristics of farmland soil pH in the past 30 years of Enshi Autonomous Prefecture, Hubei, China].

In order to explore temporal-spatial variability of farmland soil pH at Enshi Antonomous Prefecture, Hubei, China, soil pH during the past three decades was analyzed, using the datasets of the Second National Soil Survey (1980-1983) and the Cultivated Land Quality Evaluation (2010-2013). The natural and human factors inducing the change of soil pH were evaluated to provide theoretical guidance for further soil acidification management. Results showed that acidic soil (i.

Efficient, Edge-Aware, Combined Color Quantization and Dithering.

In this paper, we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function, which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on the opposite sides of an edge.