Deep CNN based MR image denoising for tumor segmentation using watershed transform

Latif, Ghazanfar; N.F, bin Awang Iskandar D.; Alghazo, Jaafar; Butt, Muhammad Mohsin; Khan, Adil H.

doi:10.14419/ijet.v7i2.3.9964

Cited by 21 publications

(5 citation statements)

References 25 publications

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“…Figure 8 shows the dilation rates in every pixelwise that produce gridding artifacts due to dilated Wireless Communications and Mobile Computing convolutions operation. Our validated dilated convolution 2D kernel is of the size3 * 3, and its approximated rate is from unity, 2 and 3, respectively [37][38][39]. Our computation is different from the actual receptive fields with separate sets of units in the inputs.…”

Section: Resultsmentioning

confidence: 99%

Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Tiwari

Kannan²,

Veeraiah

et al. 2022

Wireless Communications and Mobile Computing

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Because of the vast number of applications and the ambiguity in application methods, handwritten character recognition has garnered widespread recognition and increased prominence in the community of pattern recognition researchers ever since it was first developed. This is due to the fact that application methods can be quite ambiguous. Computer in the cloud, on the other hand, allows for suitable network access on demand to a shared pool of customizable computing resources and digital devices. According to those knowledgeable in the subject, the standard filtering techniques are not enough when it comes to the process of denoising images. In many different approaches to machine learning, information is lost not just during the filtering process itself but also at other points during the process. When a convolutional neural network is going through its pooling operation, the internal data representation either becomes misaligned or entirely vanishes (CNN). The reconstruction of low-intensity digital photographs, which takes place during repetitive filtering, breaks away the artefacts that remain after each filtering function, which results in an image that is more uniform. The multilayer wavelet transform, or MLWT, is a method for processing features that comprises of many filter bands and is used in cloud computing authorization that is protected securely. In this scenario, a significant quantity of information gets obliterated from digital photographs during the process of feature extraction and processing. These issues are investigated by the deep learning algorithms that make use of autoencoder, and the methods also handle the novel windowing blocks that are being introduced to the layers. In this section, the magnitude and phase information is considered in order to construct a deep learning framework that will provide good denoising of digital images. The proposed architecture is equipped with the capability of accurately identifying, in real time, the noise level and type that was employed in the training of the network. The method that we have proposed, which is centred on the distribution of noise, may be used to determine the kind of noise. In order to categorise the various types of noise, we investigated nine distinct noise distributions. Dilated convolutional filtering will be used as the method of choice in order to ascertain the specific nature of the noise that can be found in the digital images. An autoencoder-based deep learning algorithm is able to accomplish numerous experimental results in digital image denoising operations that are superior to those achieved by a typical deep learning algorithm even when the intensity of the scenario is low. The performance of the method that is produced by combining the autoencoder and the dilated convolutional filtering techniques is enhanced over the performance of the technology that is currently in use. Using the method that we have outlined in this study, it is possible to recreate the low-intensity images in their entirety. We were able to show that our proposed method beat other existing algorithms for low-density digital photos by comparing several metrics, such as the peak signal-to-noise ratio (PSNR), the structural similarity index, and others (SSIM).

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Section: Resultsmentioning

confidence: 99%

Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Tiwari

Kannan²,

Veeraiah

et al. 2022

Wireless Communications and Mobile Computing

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“…For weak edge-information noisy images, CNN with transformation domain method including Guan et al [63], Li et al [109], Liu et al [123], Latif et al [100] and Yang et al [204] was very popular to remove the noise. Specifically, in [123], it used wavelet method and U-net to eliminate the gridding effect of dilated convolutions on enlarging receptive field for image restoration.…”

Section: Cnn/nn and Common Feature Extraction Methods For Awni Denoisingmentioning

confidence: 99%

Deep Learning on Image Denoising: An overview

Tian

Zhang

Zheng³

et al. 2019

Preprint

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Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimating of the real noise. So far, there are little related researches to summarize different deep learning techniques for image denoising. In this paper, we make such a comparative study of different deep techniques in image denoising. We first classify the (1) deep convolutional neural networks (CNNs) for additive white noisy images, (2) deep CNNs for real noisy images, (3) deep CNNs for blind denoising and (4) deep CNNs for hybrid noisy images, which is the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of deep learning methods of different types. Next, we compare and verify the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research.

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“…Both approaches are put through their paces on two datasets: one with genuine MRI scans of knees and the other with simulated scans of the brain. These datasets will be utilized for noise removal since they include information on the intricate image space [ 15 ]. In the presented approach, a stretched convolutional neural network is combined with pre- and post-processing methods to enlarge the receptive field.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Deep CNN based MR image denoising for tumor segmentation using watershed transform

Cited by 21 publications

References 25 publications

Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Deep Learning on Image Denoising: An overview

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

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