Zhenyu Lu

TL;DR: A deep learning convolutional neural network ResNet based on the pyramid dilated convolution for Gliomas classification is proposed, integrated into the bottom of Resnet to increase the receptive field of the original network and improve the classification accuracy.

TL;DR: A flexible antenna with a simple structure, small size, and light weight for medical wearable applications is proposed, which can cover the whole ISM 5.8GHz band and perform good wearable radiation characteristics.

TL;DR: The proposed CSMS-SSRN framework can achieve better classification performance on different HSI datasets and enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification.

TL;DR: In this article, a semi-supervised learning theory and image spatial and clinical a priori knowledge of brain tumors are combined to propose a new brain-tumor segmentation method that can improve the segmentation accuracy with multiple classifier collaborative training under the premise of fewer labeled data.

TL;DR: The proposed "Intensity-Volume-LBP-PCA-KNN" method is an effective method for automatically grading gliomas and can be applied to real situations.