EVALUATION OF THE EFFECTS OF LUNGS CHEST X-RAY IMAGE FUSION WITH ITS WAVELET SCATTERING TRANSFORM COEFFICIENTS ON THE CONVENTIONAL NEURAL NETWORK CLASSIFIER ACCURACY DURING THE COVID-19 DISEASE

Background and Objective: Regarding the Coronavirus disease-2019 (COVID-19) pandemic in past years and using medical images to detect it, the image processing of the lungs and enhancement of its quality are some of the challenges in the medical image processing field. As it sounds from previous studies, the lung image processing has been raised in the other lung diseases such as lung cancer, too. Thus, the accurate classifying between normal lung image and abnormal is a challenge to aid physicians.Methods: In this paper, we have proposed an image fusion technique to increase the accuracy of classifier. In this technique, some signal preprocessing tools like discrete wavelet transform (DWT), wavelet scattering transform (WST), and image fusion by using DWT are employed to enhance ordinary convolutional neural network (CNN) classifier accuracy.Results: Unlike other studies, in this paper, different aspects of an image are fused with itself to emphasize its information which may be neglected in a total assessment of the image. We have achieved 89.8% accuracy for very simple structure of CNN classifier without using proposed fusion, and when we used proposed methods, the classifier accuracy increased to 91.8%.Conclusions: This study reveals using efficient preprocessing and presenting input images which lead to decrease the complications of deep learning classifier, and increase its accuracy overall.

Automated summary

This study introduces an image fusion technique to improve classifier accuracy by employing signal preprocessing tools like DWT, WST, and DWT-based image fusion. The fusion of different aspects of an image enhances information that may be overlooked in a total image assessment, leading to an increase in classifier accuracy from 89.8% to 91.8%.