J
Jianhua Qu
Researcher at Shandong Normal University
Publications - 14
Citations - 287
Jianhua Qu is an academic researcher from Shandong Normal University. The author has contributed to research in topics: Cluster analysis & Particle swarm optimization. The author has an hindex of 6, co-authored 14 publications receiving 114 citations.
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Journal ArticleDOI
Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques.
TL;DR: The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
Journal ArticleDOI
Deep learning-based detection and segmentation-assisted management of brain metastases.
Jie Xue,Bao Wang,Yang Ming,Xuejun Liu,Zekun Jiang,Chengwei Wang,Xiyu Liu,Ligang Chen,Jianhua Qu,Shangchen Xu,Xuqun Tang,Ying Mao,Yingchao Liu,Dengwang Li +13 more
TL;DR: The BMDS net yields the accurate detection and segmentation of BMs automatically and could assist stereotactic radiotherapy management for the diagnosis, therapy planning and follow up.
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Deep membrane systems for multitask segmentation in diabetic retinopathy
Jie Xue,Shuo Yan,Jianhua Qu,Feng Qi,Chenggong Qiu,Hongyan Zhang,Chen Meirong,Tingting Liu,Dengwang Li,Xiyu Liu +9 more
TL;DR: Evaluations on three public datasets demonstrate the robustness of the proposed novel and automatic multitask segmentation method for correctly segmenting MAs, EXs and OD in various settings.
Journal ArticleDOI
Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images
TL;DR: A novel, automatic multiorgan segmentation algorithm based on a new hybrid neural-like P system that possesses the joint advantages of cell-like and neural- like P systems and includes new structures and rules, allowing it to solve more real-world problems in parallelism.
Journal ArticleDOI
GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing
TL;DR: The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.