Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System

Abstract

As of June 2021, the World Health Organization (WHO) has reported 171.7 million confirmed cases including 3,698,621 deaths from COVID-19. Detecting COVID-19 and other lung diseases from Chest X-Ray (CXR) images can be very effective for emergency diagnosis and treatment as CXR is fast and cheap. The objective of this study is to develop a system capable of detecting COVID-19 along with 14 other lung diseases from CXRs in a fair and unbiased manner. The proposed system consists of a CXR image selection technique and a deep learning based model to classify 15 diseases including COVID-19. The proposed CXR selection technique aims to retain the maximum variation uniformly and eliminate poor quality CXRs with the goal of reducing the training dataset size without compromising classifier accuracy. More importantly, it reduces the often hidden bias and unfairness in decision making. The proposed solution exhibits a promising COVID-19 detection scheme in a more realistic situation than most existing studies as it deals with 15 lung diseases together. We hope the proposed method will have wider adoption in medical image classification and other related fields.

arXiv comment9 Pages

References in corpus (22)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SMOTE: Synthetic Minority Over-sampling Technique
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Deep Residual Learning for Image Recognition
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Densely Connected Convolutional Networks
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• COVID-19 Image Data Collection
• COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
• Xception: Deep Learning with Depthwise Separable Convolutions
• Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
• MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
• CovidAID: COVID-19 Detection Using Chest X-Ray
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
• DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
• A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
• Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
• Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays
• Chest X-rays Classification: A Multi-Label and Fine-Grained Problem