Deep convolutional neural network classified the PNEUMONIA and Coronavirus diseases (COVID-19) by softmax nonlinearity function
الباحث الأول:
Mali H. Hakem Alameady
الباحثين الآخرين:
Maryim Omran Mosa, Amir Ali Aljarrah, Huda Saleem Razzaq
المجلة:
Semnan University
تاريخ النشر:
None
مختصر البحث:
Abstract
A deep learning powerful models of machine learning indicated better performance as precision and
speed for images classification. The purpose of this paper is the detection of patients suspected of
pneumonia and a novel coronavirus. Con…
Abstract
A deep learning powerful models of machine learning indicated better performance as precision and
speed for images classification. The purpose of this paper is the detection of patients suspected of
pneumonia and a novel coronavirus. Convolutional Neural Network (CNN) is utilized for features extract
and it classifies, where CNN classify features into three classes are COVID-19, NORMAL, and
PNEUMONIA. In CNN updating weights by CNN backpropagation and SGDM optimization algorithms
in the training stage. The performance of CNN on the dataset is a combination between Chest
X-Ray dataset (1583-NORMAL images and 4272-PNEUMONIA images) and COVID-19 dataset
(126-images) for automatically anticipate whether a patient has COVID-19 or PNEUMONIA, where
accuracy 94.31% and F1-Score 88.48% in case 60% training, 20% testing, and 20% validation.
Keywords: Deep learning, convolutional neural network, and Coronavirus Disease (COVID-19).
2010 MSC: Primary 90C33; Secondary 26B25.
