Fast and Accurate COVID-19 Detection Along With 14 Other Chest Pathology Using: Multi-Level Classification.

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Abstract

COVID-19 has spread at a very fast rate and it is important to build a system that can detect it in order to help an overwhelmed health care system. The strength of deep learning techniques is used in many research studies of chest-related diseases. Although some of these researchers used state-of-the-art techniques and were able to provide promising results, these methods do not provide much benefit if they can only identify one type of disease without identifying the rest.
The main purpose of this paper is a fast and more accurate diagnosis of COVID-19. This paper proposes a technique that classifies COVID-19 using X-rays from normal X-rays and X-rays of 14 other chest diseases.
A novel multi-level pipeline was introduced based on deep learning models for the detection of COVID-19 along with other chest diseases from X-ray images. This pipeline reduces the burden of classifying a large number of classes on a single network. Deep learning models used were pre-trained models on ImageNet dataset and transfer learning was used for fast training. Lungs and heart are segmented from the whole X-ray image and passed onto the first classifier that checks if the X-ray is normal, COVID-19 affected or belongs to another chest X-ray disease. If the case is neither COVID-19 nor normal, then the second classifier comes into action and classifies the image as one of the other 14 diseases.
With our new pipeline, we show how our model makes use of the CNN state-of-the-art deep neural networks to achieve COVID-19 classification accuracy with 14 other chest diseases and normal X-ray images that are competitive with present state-of-the-art models. Due to lack of data in some classes like COVID-19, 10-fold cross-validation was applied. Our classification technique achieved average accuracy after 10-fold cross validation through the ResNet50 model with training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (3 classes). For the second level of classification (14 classes), we achieved maximum training accuracy of 88.52% and test accuracy of 66.634% using ResNet50. We also showed that when classifying all the 16 classes at once, overall accuracy decreased for detection of COVID-19 which in the case of ResNet50 was 88.92% for training data and 71.905% for test data.
This paper proposed a pipeline that detected COVID-19 with a higher accuracy along with 14 other chest diseases using X-rays. We showed that our pipeline provides better accuracy by dividing the classification task into multiple steps rather than classifying them collectively.