Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images

2020-10-07

Anais do XVI Workshop de Visão Computacional (WVC 2020)

AI Summary

In early COVID-19 screening, the scarcity of positive chest X-ray samples and poor model generalizability hinder reliable automated diagnosis. Method: We developed a binary classification system for COVID-19 detection from chest X-ray images, proposing a shallow fine-tuning strategy tailored to few-shot learning. This approach integrates data augmentation and rigorous 10-fold cross-validation to comparatively evaluate four CNN architectures—AlexNet, VGG-11, SqueezeNet, and DenseNet-121. Contribution/Results: All models achieved ≥97.00% accuracy; notably, the lightweight SqueezeNet attained 99.20%, outperforming others significantly. With only 3.2% of DenseNet-121’s parameter count, SqueezeNet demonstrated superior generalization under limited training data and greater deployment feasibility on resource-constrained devices. Our work empirically validates shallow fine-tuning as an effective strategy for few-shot medical image classification, offering a practical, rapid, and high-accuracy AI-assisted diagnostic solution for low-resource clinical settings.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Large Vision Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.

Problem

• COVID-19 detection
• Convolutional Neural Networks
• Chest X-ray

Innovation

• Convolutional Neural Networks
• COVID-19 Detection
• SqueezeNet Optimization