IEEE Access | |
Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network | |
Min Xu1  Tianyang Wang2  Jing Zhang3  Chao Cheng4  Rahul Upadhyay5  Vinay Kumar5  Aviral Chharia6  | |
[1] Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA;Department of Computer Science and Information Technology, Austin Peay State University, Clarksville, TN, USA;Department of Computer Science, University of California at Irvine, Irvine, CA, USA;Department of Medicine, Baylor College of Medicine, Houston, TX, USA;Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India;Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India; | |
关键词: Deep learning; COVID-19; medical imaging; computer-aided diagnosis; pandemics; | |
DOI : 10.1109/ACCESS.2022.3153059 | |
来源: DOAJ |
【 摘 要 】
Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the
【 授权许可】
Unknown