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.
Highlights
T HROUGHOUT human history, there have been several pandemics, the most recent and ongoing being the Severe Acute Respiratory Syndrome (SARS)-CoV-2
We demonstrate how the recent rise of deep learning (DL)-based supervised feature extraction bridges the gap between fuzzy reflex classifiers and their machine learning (ML) counterparts
We discuss the experiments and the results of the model’s ability to detect novel diseases on three unseen novel disease datasets: SARS-CoV-1, MERS-CoV, and COVID-19 to demonstrate the strong generalizability of the approach
Summary
T HROUGHOUT human history, there have been several pandemics, the most recent and ongoing being the SARS-CoV-2. The first case of the disease was reported in late December 2019 in Wuhan, China [1], [2]. Ever since the first case of COVID-19 was reported, the world has seen over 219 million cases and 4.547 million deaths due to the virus [3]. Despite tremendous improvements in our understanding of microbial hazards over the last two decades, humans remain as vulnerable as ever to unexpected attacks by unknown. Chharia et al.: Deep-Precognitive Diagnosis: Novel Disease Detection with Biologically-inspired Conv-Fuzzy Network. June 2012, Jeddah, Saudi Arabia Dec 2019, Wuhan, China.
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