Deep-precognitive diagnosis: preventing future pandemics by novel disease detection With biologically-inspired conv-fuzzy network

Authors

Aviral Chharia, Thapar Institute of Engineering & Technology
Rahul Upadhyay, Thapar Institute of Engineering & Technology
Vinay Kumar, Thapar Institute of Engineering & Technology
Chao Cheng, Baylor College of Medicine
Jing Zhang, University of California, Irvine
Tianyang Wang, Austin Peay State University
Min Xu, Carnegie Mellon University & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Access

Abstract

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 first 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 de novo 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. Second, 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 Page

23167

Last Page

23185

DOI

10.1109/ACCESS.2022.3153059

Publication Date

2-21-2022

Keywords

computer-aided diagnosis, COVID-19, Deep learning, medical imaging, pandemics

Comments

Open Access version with thanks to IEEE and IEEE Access

License: CC BY 4.0

Uploaded 30 March 2022

Recommended Citation

A. Chharia et al., "Deep-Precognitive Diagnosis: preventing future pandemics by novel disease detection with biologically-inspired Conv-Fuzzy network," in IEEE Access, vol. 10, pp. 23167-23185, 2022, doi: 10.1109/ACCESS.2022.3153059.