1. A critic evaluation of methods for COVID-19 automatic detection from X-ray images
- Author
-
Gianluca Maguolo and Loris Nanni
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Coronavirus disease 2019 (COVID-19) ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Convolutional neural network ,Article ,Machine Learning (cs.LG) ,Task (project management) ,Testing protocols ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Protocol (science) ,Measure (data warehouse) ,Covid-19 diagnosis ,Artificial neural network ,business.industry ,Image and Video Processing (eess.IV) ,020206 networking & telecommunications ,Pattern recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Hardware and Architecture ,Signal Processing ,X-Ray images ,X ray image ,Convolutional neural networks ,020201 artificial intelligence & image processing ,Artificial intelligence ,Covid-19 ,business ,Software ,Information Systems - Abstract
In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose. more...
- Published
- 2021
- Full Text
- View/download PDF