Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques

Deep Learning (DL) techniques leverage the rich data diversity in the modern era of Big Data, allowing possibilities of achieving human-level performances in many real-world problems. However, newly emerged problems such as the ongoing COVID-19 pandemic have underscored the challenge of acquiring a...

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Main Author:	Lee Kin Wai
Format:	Thesis
Language:	English English
Published:	2023
Subjects:	TJ1-1570 Mechanical engineering and machinery
Online Access:	https://eprints.ums.edu.my/id/eprint/40749/1/24%20PAGES..pdf https://eprints.ums.edu.my/id/eprint/40749/2/FULLTEXT..pdf
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spelling	my-ums-ep.407492024-09-05T01:49:22Z Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques 2023 Lee Kin Wai TJ1-1570 Mechanical engineering and machinery Deep Learning (DL) techniques leverage the rich data diversity in the modern era of Big Data, allowing possibilities of achieving human-level performances in many real-world problems. However, newly emerged problems such as the ongoing COVID-19 pandemic have underscored the challenge of acquiring a larger corpus of data during the early stages of the pandemic due to the availability of samples, data protection policies, labour, and facility resources. Furthermore, the effectiveness of existing DL models that are trained on older datasets might be vulnerable to the continuous emergence of COVID-19 variants that may potentially result in distribution shifts. This research introduces a data synthesisation framework named stacked residual dropout generative adversarial network (sRD-GAN), which alleviates the problem of data paucity by generating synthetic lung medical images that contain precise radiographic feature annotations. The underlying design of sRD-GAN is an Image-to-Image translation setting that facilitates instance-level diversity via the latent space stochasticity induced by the novel stacked residual dropout (sRD) regularization. To this end, experiments show that sRD-GAN achieved perceptually significant structural dissimilarities of the ground glass opacities (GGO) from diverse COVID-19 CT images without disentangling the content-style attributes of the images as in conventional multimodal image translation techniques. Since the sRD regularization is a strategic incorporation of the conventional dropout regularization, which can be generalized across neural network models, the sRD regularization can be easily incorporated into existing image synthesizer models without modifying the original setup of these models. In addition, a new training loss function known as adaptive pixel consistency loss is proposed for effective noise reduction by encouraging structural similarity of the invariance features of the images from both domains. Quantitative results show that the synthetic COVID-19 CT images achieve a promising Fréchet Inception Distance (FID) of 58.68, which is superior to existing GAN baselines such as GAN (157.18), CycleGAN (115.14), and One-to-one CycleGAN (94.11). Visual examination of the synthetic images also indicates excellent perceptual image quality and realism, where synthetic radiographic features of GGO achieve consistency with real COVID-19 CT images examined by an experienced radiologist. Furthermore, the effectiveness of the proposed sRD-GAN is also validated on Community-Acquired Pneumonia (CAP) CT images and COVID-19 X-Ray images, which achieved comparable performances with COVID-19 CT images. This suggests that the proposed method can be easily extended to other similar applications. Lastly, the sRD-GAN is applied to the problem of COVID-19 disease recognition in the form of dynamic data augmentation. Empirical results suggest that synthetic images can approximate real data distribution for model training purposes. Specifically, the VGG19 models achieve the highest accuracy score at 97.54% on the test set when training with fully synthetic COVID-19 CT images in 3000-images dataset size, contributing to a 12.95% accuracy improvement from training with only real image data. 2023 Thesis https://eprints.ums.edu.my/id/eprint/40749/ https://eprints.ums.edu.my/id/eprint/40749/1/24%20PAGES..pdf text en public https://eprints.ums.edu.my/id/eprint/40749/2/FULLTEXT..pdf text en validuser masters Universiti Malaysia Sabah Faculty of Engineering
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TJ1-1570 Mechanical engineering and machinery
spellingShingle	TJ1-1570 Mechanical engineering and machinery Lee Kin Wai Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
description	Deep Learning (DL) techniques leverage the rich data diversity in the modern era of Big Data, allowing possibilities of achieving human-level performances in many real-world problems. However, newly emerged problems such as the ongoing COVID-19 pandemic have underscored the challenge of acquiring a larger corpus of data during the early stages of the pandemic due to the availability of samples, data protection policies, labour, and facility resources. Furthermore, the effectiveness of existing DL models that are trained on older datasets might be vulnerable to the continuous emergence of COVID-19 variants that may potentially result in distribution shifts. This research introduces a data synthesisation framework named stacked residual dropout generative adversarial network (sRD-GAN), which alleviates the problem of data paucity by generating synthetic lung medical images that contain precise radiographic feature annotations. The underlying design of sRD-GAN is an Image-to-Image translation setting that facilitates instance-level diversity via the latent space stochasticity induced by the novel stacked residual dropout (sRD) regularization. To this end, experiments show that sRD-GAN achieved perceptually significant structural dissimilarities of the ground glass opacities (GGO) from diverse COVID-19 CT images without disentangling the content-style attributes of the images as in conventional multimodal image translation techniques. Since the sRD regularization is a strategic incorporation of the conventional dropout regularization, which can be generalized across neural network models, the sRD regularization can be easily incorporated into existing image synthesizer models without modifying the original setup of these models. In addition, a new training loss function known as adaptive pixel consistency loss is proposed for effective noise reduction by encouraging structural similarity of the invariance features of the images from both domains. Quantitative results show that the synthetic COVID-19 CT images achieve a promising Fréchet Inception Distance (FID) of 58.68, which is superior to existing GAN baselines such as GAN (157.18), CycleGAN (115.14), and One-to-one CycleGAN (94.11). Visual examination of the synthetic images also indicates excellent perceptual image quality and realism, where synthetic radiographic features of GGO achieve consistency with real COVID-19 CT images examined by an experienced radiologist. Furthermore, the effectiveness of the proposed sRD-GAN is also validated on Community-Acquired Pneumonia (CAP) CT images and COVID-19 X-Ray images, which achieved comparable performances with COVID-19 CT images. This suggests that the proposed method can be easily extended to other similar applications. Lastly, the sRD-GAN is applied to the problem of COVID-19 disease recognition in the form of dynamic data augmentation. Empirical results suggest that synthetic images can approximate real data distribution for model training purposes. Specifically, the VGG19 models achieve the highest accuracy score at 97.54% on the test set when training with fully synthetic COVID-19 CT images in 3000-images dataset size, contributing to a 12.95% accuracy improvement from training with only real image data.
format	Thesis
qualification_level	Master's degree
author	Lee Kin Wai
author_facet	Lee Kin Wai
author_sort	Lee Kin Wai
title	Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
title_short	Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
title_full	Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
title_fullStr	Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
title_full_unstemmed	Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
title_sort	synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty of Engineering
publishDate	2023
url	https://eprints.ums.edu.my/id/eprint/40749/1/24%20PAGES..pdf https://eprints.ums.edu.my/id/eprint/40749/2/FULLTEXT..pdf
_version_	1811770550339502080

Synthesisation of diverse and photorealistic lung images for lung diseases recognition using deep learning techniques

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