Restoring Lesions in Low-Dose Computed Tomography Images of COVID-19 Using Deep Learning

Kulathilake, K.A.S.H.; Abdullah, Nor Aniza; Lachyan, Abhishek; Bandara, A. M. R. Ravimal; Patel, Dhrumil Deveshkumar; Lai, Khin Wee

doi:10.1007/978-3-030-90724-2_44

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Many computerized techniques have been introduced for COVID-19 in recent years by researchers of computer vision ( 27 ). Several researchers focused on traditional techniques and few of them using deep learning architectures for the detection and classification of COVID-19 from chest X-ray images.…”

Section: Related Studymentioning

confidence: 99%

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Hamza

Khan

Wang

et al. 2022

Front. Public Health

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Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

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Section: Related Studymentioning

confidence: 99%

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Hamza

Khan

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

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Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN

Liu

Kang

et al. 2022

XST

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BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to considerable noise in the reconstructed image that can affect doctor’s judgment. OBJECTIVE: To solve this problem, this study proposes a local total variation and improved wavelet residual convolutional neural network (LTV-WRCNN) denoising model. METHODS: The model first introduces local total variation (LTV) to decompose the LDCT image into cartoon and texture image. Next, the texture image is filtered using the non-local mean (NLM). Then, the cartoon image is added to the filtered texture image to obtain the preprocessing image. Finally, the pre-processed image is fed into the improved wavelet residual neural network (WRCNN) to obtain an improved image. Additionally, we also introduce a compound loss in wavelet domain that combines mean squared error loss and directional regularization loss to separate the structural details from noise more thoroughly. RESULTS: Compared with state-of-the-art methods, the peak-signal-to-noise ratio (PSNR) value and the structure similarity (SSIM) value of the processed CT images using the new proposed model are 33.4229 dB and 0.9158. Study also shows that applying new model obtains better results visually and numerically, especially in terms of the preservation of structural details. CONCLUSIONS: The proposed new model is feasible and effective in improving the quality of LDCT images.

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Restoring Lesions in Low-Dose Computed Tomography Images of COVID-19 Using Deep Learning

Cited by 2 publications

References 13 publications

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN

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