Practical Machine Learning Techniques for COVID-19 Detection Using Chest

This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which were pre-trained with ImageNet. We compared this approach with the classical machine learning approaches, namely Support Vector Machine (SVM) and Random Forest. In addition to independently extracting image features, pre-trained features obtained from a VGG19 model were utilized with these classical machine learning techniques. Both binary and categorical (multi-class) classification tasks were considered on classical machine learning and deep learning. Several X-ray images ranging from 7000 images up to 11500 images were used in each of our experiments. Five experimental cases were considered for each classification model. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach produced up to 97.5% accuracy and 98% F1-score on COVID-19 vs. non-COVID-19 (normal or diseases excluding COVID-19) class, while in classical machine learning approaches, the SVM with pretrained features produced 98.9% accuracy, and at least 98.2% precision, recall and F1-score on COVID-19 vs. non-COVID-19 class. These disease detection models can be deployed for practical usage in the near future.

COVID-19 and Other Lung Disease Detection Using VGG19 Pretrained Features and Support Vector Machine

At present, pandemic phase is declared by World Health Organization caused by COVID-19 disease that endangers all walks of life. The disease has spread quickly around the world causing many countries to lockdown. The medical center could not handle a large number of infected patients. To effectively and automatically classify the infected patients is a big challenge. So, we introduce an efficient lung disease detection method that can detect and identify normal people (without lung disease) and others who have lung disease(s) using chest X-ray images. We consider many well-known lung diseases which are COVID-19, Pneumonia, Pneumothorax, and Atelectasis. First, we preprocessed the images and performed feature extraction using a VGG19 deep-learning model, and then used a Support Vector Machine as the classification model. The dataset that we used is publicly provided with many types of diseases. Our model obtains great results on binary class classification considering COVID-19 and non-COVID-19 classes with 99.0% accuracy, 98.3% recall, 99.1% precision, and 98.7% f1-score. With multi-class classification (5 output classes), we obtain 99.2% accuracy for COVID-19 detection, 99.2% accuracy for Pneumonia, 85.4% accuracy for Atelectasis, and 84.8% accuracy for Pneumothorax. © 2021 IEEE.

Lung Disease Detection and Classification with Deep Learning Approach

Nowadays, COVID-19 outbreak and respiratory symptoms globally take a huge number of people's lives away. Especially, COVID-19, which is a pandemic initially spreading out in the first quarter of the year 2020, heavily affects many people to die. Most countries have tried to find ways to solve and mitigate this outbreak including respiratory diseases due to the mentioned reason. We also face with insufficient number of medical personnel and equipment to treat the diseases. The need of technology to analyze the images for the disease detection is quite a challenge. In this work, we consider detecting and classifying many lung diseases from chest X-ray images using a deep learning (artificial intelligence) approach with VGG16 models. The lung diseases are COVID-19, Pneumonia and Pneumothorax. We use quite large published disease datasets. Our detection and classification models give impressive results providing between 93% and 100% accuracy, precision, recall and F1-measure. © 2021 IEEE.