Open AccessA Novel Deep Learning Pipeline Architecture based on CNN to Detect Covid-19 in Chest X-ray Images
Author(s) -
Saqib Jamal Syed Putra Sumari
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.4804
Subject(s) - convolutional neural network , computer science , deep learning , artificial intelligence , covid-19 , pipeline (software) , artificial neural network , architecture , pattern recognition (psychology) , image (mathematics) , machine learning , medicine , infectious disease (medical specialty) , disease , pathology , geography , archaeology , programming language
Covid-19 is a severe public health problem worldwide. To date, it has spanned worldwide, with 24.6 million infected with 835,843 confirm the death. Covid-19 detection is indeed an important task and has to be done as quickly as possible so that treatment and monitoring can be carried out early. The current world standard RT-PCR screening for Covid-19 detection has to cope with the world population's great demand. There is a need to have an alternative way to cope with the demands. It has to be a quick and accurate detection procedure, such as using a chest x-ray for Covid-19 detection. This paper proposes a deep learning pipeline architecture called Gray Level Co-occurrence Matrix GLCM) with Convolutional Neural Network (CNN) for Covid-19 detection using chest X-ray image. The proposed method has two main diagnosis features, a quicker diagnosis, and a detailed diagnosis. The quicker diagnosis uses few GLCM features and a standard neural network (NN) algorithm to detect Covid-19 symptoms. It is a suitable method for rural areas where computing resources are minimal. The detailed diagnosis uses huge image pixel features and a deep convolutional neural network (CNN) algorithm to detect Covid-19 symptoms. It is a suitable method for places where computing resources are sufficient. The proposed work provides the highest classification performance, with 97.06% accuracy compared to other similar works.