Haowei Yang

Qishi Zhan, Dan Sun, Erdi Gao et al.

This study introduces a novel unsupervised medical image feature extraction method that employs spatial stratification techniques. An objective function based on weight is proposed to achieve the purpose of fast image recognition. The algorithm divides the pixels of the image into multiple subdomains and uses a quadtree to access the image. A technique for threshold optimization utilizing a simplex algorithm is presented. Aiming at the nonlinear characteristics of hyperspectral images, a generalized discriminant analysis algorithm based on kernel function is proposed. In this project, a hyperspectral remote sensing image is taken as the object, and we investigate its mathematical modeling, solution methods, and feature extraction techniques. It is found that different types of objects are independent of each other and compact in image processing. Compared with the traditional linear discrimination method, the result of image segmentation is better. This method can not only overcome the disadvantage of the traditional method which is easy to be affected by light, but also extract the features of the object quickly and accurately. It has important reference significance for clinical diagnosis.

Houze Liu, Iris Li, Yaxin Liang et al.

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points, respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode.