Chen Li
Analysis Group
126 Papers
359 Citations
Chen Li is an academic researcher from Analysis Group. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 14, co-authored 100 publications. Previous affiliations of Chen Li include Northeastern University & Northeastern University (China).
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Papers
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.
Mamunur Rahaman,Chen Li,Yu-Dong Yao,Frank Kulwa,Mohammad Asadur Rahman,Qian Wang,Shouliang Qi,Fanjie Kong,Xuemin Zhu,Xin Zhao +9 more
TL;DR: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Xiaomin Zhou,Chen Li,Mamunur Rahaman,Yu-Dong Yao,Shiliang Ai,Changhao Sun,Qian Wang,Yong Zhang,Mo Li,Xiaoyan Li,Tao Jiang,Dan Xue,Shouliang Qi,Yueyang Teng +13 more
TL;DR: This review presents a comprehensive overview of the BHIA techniques based on ANNs, and categorizes the existing models into classical and deep neural networks for in-depth investigation.
188
LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation
Jinghua Zhang,Chen Li,Sergey Kosov,Marcin Grzegorzek,Kimiaki Shirahama,Tao Jiang,Changhao Sun,Changhao Sun,Zihan Li,Hong Li +9 more
TL;DR: In this article, a low-cost U-Net (LCU-Net) was proposed for the EM image segmentation task to assist microbiologists in detecting and identifying EMs more effectively.
176
DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.
TL;DR: Mamunur et al. as discussed by the authors proposed DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL, to classify the cervical cells accurately, which achieved the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class and 5-class classification.
176
An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification
Dan Xue,Xiaomin Zhou,Chen Li,Yu-Dong Yao,Mamunur Rahaman,Jinghua Zhang,Hao Chen,Jinpeng Zhang,Shouliang Qi,Hongzan Sun +9 more
TL;DR: An Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images and a weighted voting based EL strategy is introduced to enhance the classification performance.