Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images


Yakoop Razzaz Hamoud Qasim, Habeb Abdulkhaleq Mohammed Hassan, Abdulelah Abdulkhaleq Mohammed Hassan, Taiz University, Yemen


In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar.


Deep Learning, Concatenation Technique, Convolutional Neural Networks, COVID-19, Transfer Learning.

Full Text  Volume 10, Number 16