[Paper Review] Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassasni+3 more|arXiv (Cornell University)|Apr 22, 2020

COVID-19 diagnosis using AI41 references62 citations

TL;DR

The paper evaluates a transfer-learning based deep CNN feature extractor ensemble followed by traditional ML classifiers to detect COVID-19 from chest X-ray and CT images, achieving up to 99% accuracy on a public dataset.

ABSTRACT

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

Motivation & Objective

• Demonstrate a generic, non-handcrafted feature extraction approach using deep CNNs for COVID-19 classification on X-ray and CT images.
• Assess whether transferring knowledge from large pre-trained CNNs improves detection with limited COVID-19 data.
• Avoid extensive pre-processing to promote generalization across heterogeneous imaging sources.
• Identify combinations of CNN feature extractors and ML classifiers that maximize accuracy and efficiency.
• Provide a web-based CAD aid for rapid screening of suspected cases.

Proposed method

• Use pre-trained CNN architectures (MobileNet, DenseNet, Xception, InceptionV3, InceptionResNetV2, ResNet, VGGNet, NASNet) as feature extractors (transfer learning) to encode images into low-dimensional feature vectors.
• Train multiple traditional ML classifiers (Decision Tree, Random Forest, XGBoost, AdaBoost, Bagging, LightGBM) on the CNN-derived features.
• Evaluate with 10-fold cross-validation on a public dataset of chest X-ray and CT images; weights initialized from ImageNet.
• Apply two minimal pre-processing steps: resizing to uniform dimensions and image normalization (ImageNet mean subtraction and min-max normalization).
• Report accuracy, precision, recall, and F1-score to compare CNN+ML combinations.

Experimental results

Research questions

• RQ1 How well do deep CNN-derived feature representations combined with ML classifiers detect COVID-19 vs healthy controls in X-ray and CT images?
• RQ2 Which CNN architectures and ML classifiers yield the highest accuracy and reliability for this task?
• RQ3 Does minimal pre-processing and transfer learning provide robust generalization across heterogeneous image sources?
• RQ4 What are the trade-offs in computation time between feature extraction and classifier training for practical CAD deployment?

Key findings

• The best result is 99.00% accuracy (±0.09) using DenseNet121 features with a Bagging classifier.
• A close second is 98.00% accuracy (±0.09) with DenseNet121 features (or ResNet50 features) paired with LightGBM.
• Top precision, recall, and F1-scores (99.00%) are achieved with MobileNet and InceptionV3 features when using Bagging as the classifier.
• Overall results show that deep CNN features (DenseNet121/DenseNet201/MobileNet/Xception/InceptionV3) with Bagging or XGBoost classifiers outperform several other CNN+ML combinations on the provided dataset.
• Extraction and training times indicate the approach is faster than training very deep CNNs from scratch, enabling near real-time inference for CAD systems.
• A web-based detection tool was implemented to simulate a clinical screening pipeline, though clinical validation is still required.