Fast and denoise feature extraction based ADMF–CNN with GBML framework for MRI brain image

Sreelakshmi, D.; Inthiyaz, Syed

doi:10.1007/s10772-020-09793-w

Cited by 21 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to previous works, this indicates that our proposed method is more efcient in restoring original images from 50% Gaussian noise. Furthermore, our study has achieved an impressive PSNR value not only higher than previous works but also when compared to the same type of noise (Gaussian 50%), such as Sreelakshmi et al [11] (PSNR 48.68). Another signifcant diference is that we have performed denoising of brain MRI images with a relatively high noise level (Gaussian 50%), while some previous studies focused on weaker noise.…”

Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 50%

“…Te authors in the study by Sreelakshmi et al [11] proposed a method that combines model of adaptive median flter (ADMF) and convolutional neural networks (CNN) to solve the noise problem in MRI images. Tis method also uses a machine learning system based on Gradient Boosting Machine Learning (GBML) algorithm to classify and extract features from MRI images, thereby helping to improve the quality of MRI images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Han,

Nguyen Van,

Nguyen Huu

2024

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Noise is an undesirable and disturbing effect that degrades the quality of an image. The importance of noise reduction in images and its wide-ranging applications are essential. Most popular image noise filters rely on static parameters that are often challenging to fine-tune. Dynamically adapting these static parameters for image noise filters is a critical area of research. In this study, a combination model between the features of complex networks and artificial neural networks is proposed to automatically find the noise reduction parameter of the block-matching and 3D filtering method. Experimental results on the black and white MRI image set have shown that the model correctly predicted the parameters of the BM3D filter and removed the noise in the images of those MRI images. The model gave high denoising results with PSNR of 51.94 and SSIM of 0.998.

show abstract

Section: Calculation Time Using Our Proposed Model Inmentioning

confidence: 50%

Section: Related Workmentioning

confidence: 99%

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Han,

Nguyen Van,

Nguyen Huu

2024

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…This study shows that CNNs offer a better approach to skin cancer spotting, and that the image acquisition step plays an important role in algorithm execution [14]. [15] showed that practicing pre-trained networks outperforms DCNN from scratch in the absence of labelled input data. In lieu of establishing a comprehensive CNN from scratch, we fortuitously approached the problem by confining a network trained on images about specific knowledge of medical scenarios.…”

Section: IImentioning

confidence: 83%

Skin Lesions Detection using Deep Learning Techniques

Sundari¹,

AK²,

Islabudeen³

2023

IJRASET

View full text Add to dashboard Cite

The main category of cancer is skin cancer, which is manifested by certain skin diseases. They have been constrained into various typologies stationed on morphological features, color, structure, and texture. Due to certain factors, the study equips analysis efforts to advance algorithms with greater closeness and tensility in detecting early-stage melanoma. As per the ACS report, melanoma is one of the recurrent cancers in the world. In 2017, around 87,110 new cases were diagnosed in the United States alone. Dermoscope images contain imperfections such as shadows, artifacts, and noise that degrade image essence. To overthrow these objection, deep learning neural networks have been used to detect skin cancer by tampering images. In the proposed work, automatic classification of skin lesions is proposed. Image classification, object detection etc are some of the computer vision tasks in deep CNN which have driven DCNNs to be reliable on several substitute assumptions, tiling its way for new exploration areas. CNN attained performance equivalent to all experts tested, achieving an exact competence equivalent to dermatologists treating skin cancer. In this article, we attempt to improve the Deep Convolutional Neural Networks example using the ImageNet dataset with HAM10000, fortuitously classifying seven categories of skin lesions. HAM10000 is a dataset of 10000 dermoscope images. Layers are fine-tuned applying separate approach such as InceptionV3, InceptionResNet, DenseNet and VGG-16. Over previous years, the power of deep learning-based approaches has enhanced fiercely and their work come out to outperform common image processing approach on classification tasks. However, these categories of machine learning-based accession have important drawbacks. Training requires thousands of annotated images for each class. The idea is to use deep learning algorithms and available dataset assets to bear models with higher accuracy and best results.

show abstract

“…Apart from these, a ten-layer CNN [102], multi-channel residual learning CNN [103], CNN-DMRI [104], HydraNet [105], NNDnet [106], CMGDNet [107], 3D-Parallel-RicianNet [108], and a patch-based CNN [109] have been developed for accurate MRI denoising. Several other recent works incorporated CNN-based solutions for brain MRI denoising [110][111][112][113][114][115].…”

Section: Noise In Anatomical Mrimentioning

confidence: 99%

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

et al. 2022

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

show abstract

Fast and denoise feature extraction based ADMF–CNN with GBML framework for MRI brain image

Cited by 21 publications

References 19 publications

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Denoising Method for MRI Images Using Modified BM3D Filter with Complex Network and Artificial Neural Networks

Skin Lesions Detection using Deep Learning Techniques

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

Contact Info

Product

Resources

About