Denoising Medical Images Using Deep Learning in IoT Environment

More, Sujeet; Singla, Jimmy; Song, Ohyoung; Tariq, Usman; Malebary, Sharaf J.

doi:10.32604/cmc.2021.018230

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…As can be observed in Figure 6 , the BPI values of the denoised ultrasound images processed using the proposed technique were all high and far above the experimental threshold of 0.45. For denoised ultrasound images, the methods of Saeed Izadi et al (2022) [ 6 ], Thayammal et al (2021) [ 5 ], and Sujeet More et al (2021) [ 8 ] achieve BPI values of 0.40–0.45. For ultrasound images, the Nguyen Thanh-Trung et al (2021) [ 11 ] method has a lower BPI, with a maximum value of just 0.33, while the Dihan Zheng et al (2021) [ 14 ] algorithm consistently has a BPI below 0.2.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning-based image noise-removal algorithms are effective but restricted by sample. We develop a deep feed forward noise-removal CNN for medical image noise removal using a modest sample set to demonstrate a new method for solving basic eyesight issues [ 8 ]. We propose a different training program that effectively adapts, initially conceived for unsupervised learning, to the activities of image noise removal and blind inpainting.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Graphical view of image noise removal comparison chart of proposed and state-of-art methods [ 5 , 6 , 8 , 9 , 11 , 14 ]. …”

Section: Figurementioning

confidence: 99%

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Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Vimala

Srinivasan

Mathivanan

et al. 2023

Sensors

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Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

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