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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/NQ.2022.20.11.NQ66036

Deeplearningapproachfor detection of multiple respiratory diseasesfrom chest X-rayanalysis:Asurvey

SandipBuradkar, Dr. Prashad Joshi, Dr. PradnyaGhare

Abstract

Deep learning approach for detecting various respiratory diseases hasbeen challenging and mostdemanding research area. Withrapidly increase in number of patients suffering from respiratory diseases quick method hasbecome necessary for classification and detection of respiratory diseases. This survey paper offers a comparative study of various deep learning techniques that can use chest Xraysfordetection of various thoracic diseases.There is possibility of severe respiratory failure in some thoracic diseases if they are not treated in initial stages. Many digital image processing techniques ,machine learning and deep learning models have been developed for this purpose[17]. Different forms of existing deep learning techniques including convolutional neural network (CNN), visual geometry group based neural network (VGG-16 and VGG-19) have been developed for respiratory disease prediction. But these all models have some limitations that they do not cover all respiratory diseases including Covid-19, Viral pneumonia and Tuberoculosis on single platform. Therefore, we propose our customized new deep learning model Clx-Net by using data augmentation technique to enlarge the area of available dataset[1][2] to make model more efficient with less time consumption per epoch and provide localization to identify infected region by examining chest X-ray images. Our focus is to develop a new unique deep learning based model Clx-Net which will be able to detect almost all major respiratorydiseases including Covid-19. It will simplify the detection of respiratory diseases and also find the location of infected chest area to make task easy for radiologists.

Keywords

Deep learning Thoracic disease CNN GG Data augmentation.

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