Open AccessImplementation of convolutional neural network approach for COVID-19 disease detection
Author(s) -
Emrah Irmak
Publication year - 2020
Publication title -
physiological genomics/physiological genomics (print)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.078
H-Index - 112
eISSN - 1531-2267
pISSN - 1094-8341
DOI - 10.1152/physiolgenomics.00084.2020
Subject(s) - convolutional neural network , computer science , hyperparameter , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , image (mathematics) , covid-19 , grid , hyperparameter optimization , data set , contextual image classification , deep learning , data mining , mathematics , disease , support vector machine , pathology , medicine , infectious disease (medical specialty) , geometry , programming language
In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author's knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.