Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9718075/ |
| _version_ | 1811309072621764608 |
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| author | Aviral Chharia Rahul Upadhyay Vinay Kumar Chao Cheng Jing Zhang Tianyang Wang Min Xu |
| author_facet | Aviral Chharia Rahul Upadhyay Vinay Kumar Chao Cheng Jing Zhang Tianyang Wang Min Xu |
| author_sort | Aviral Chharia |
| collection | DOAJ |
| description | 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 <italic>first</italic> to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A <italic>de novo</italic> biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. <italic>Second,</italic> the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks. |
| first_indexed | 2024-04-13T09:35:58Z |
| format | Article |
| id | doaj.art-b834639114b245cf98eb73622f090a5d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:35:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b834639114b245cf98eb73622f090a5d2022-12-22T02:52:06ZengIEEEIEEE Access2169-35362022-01-0110231672318510.1109/ACCESS.2022.31530599718075Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy NetworkAviral Chharia0https://orcid.org/0000-0003-4662-9747Rahul Upadhyay1https://orcid.org/0000-0003-0476-4529Vinay Kumar2https://orcid.org/0000-0001-9086-4782Chao Cheng3https://orcid.org/0000-0002-5002-3417Jing Zhang4Tianyang Wang5https://orcid.org/0000-0003-3184-0566Min Xu6https://orcid.org/0000-0002-0881-5891Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaElectronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaElectronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Medicine, Baylor College of Medicine, Houston, TX, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science and Information Technology, Austin Peay State University, Clarksville, TN, USAComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USADeep 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 <italic>first</italic> to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A <italic>de novo</italic> biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. <italic>Second,</italic> the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.https://ieeexplore.ieee.org/document/9718075/Deep learningCOVID-19medical imagingcomputer-aided diagnosispandemics |
| spellingShingle | Aviral Chharia Rahul Upadhyay Vinay Kumar Chao Cheng Jing Zhang Tianyang Wang Min Xu Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network IEEE Access Deep learning COVID-19 medical imaging computer-aided diagnosis pandemics |
| title | Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network |
| title_full | Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network |
| title_fullStr | Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network |
| title_full_unstemmed | Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network |
| title_short | Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network |
| title_sort | deep precognitive diagnosis preventing future pandemics by novel disease detection with biologically inspired conv fuzzy network |
| topic | Deep learning COVID-19 medical imaging computer-aided diagnosis pandemics |
| url | https://ieeexplore.ieee.org/document/9718075/ |
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