An adaptive method for edge preserving denoising

Bhargava, Neeraj; Sharma, Arpit; Kumar, Abhishek; Rathoe, Pramod Singh

doi:10.1109/cesys.2017.8321149

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…With regard to Study I and Study II ROC curves at various edge settings, the TPR is compared to the FPR. Affectability, survey, and chance of acknowledgment are all terms used to describe the true positive rate [ 24 , 31 , 32 ]. The likelihood of feigned caution is frequently phrased as 1 distinction.…”

Section: Resultsmentioning

confidence: 99%

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Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection

Sharma

Nandal

Koundal

et al. 2022

BioMed Research International

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This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively.

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Section: Resultsmentioning

confidence: 99%

“…(1) Filter for Morphological Restoration . The standard presmoothing filter reduces noise and irregular features well, but it loses contour edge information, causing a change in the region contour [ 24 ]. The edge shape data of the objective can be very much protected when filtering and denoising the MRI picture of the chest tumor.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection

Sharma

Nandal

Koundal

et al. 2022

BioMed Research International

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“…To accomplish clearness in pictures, the Average separating strategy is utilized. Then, at that point, a pixel-based division strategy is utilized to recognize a brain picture and another influenced region [6]. Features Such as the MEAN, PSNR, Entropy, and Standard deviation were extricated from the sectioned picture and afterward, the order utilizing extricated highlights is finished utilizing CNN [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…They affect a variety of vital organs, and can potentially result in death. Classification is the process of determining whether a brain image is benign or cancerous [5][6][7]. Various features are extracted during this process.…”

Section: Introductionmentioning

confidence: 99%

A Novel Brain Tumor Classification Algorithm using SVM Classifier

Sharma¹,

Nandal²,

Sinhal³

2022

IJETAE

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This study provides an intelligent classification method for distinguishing between abnormal and normal MRI brain images. Medical pictures like MRI, ECG, and CTscan pictures are vital tools for accurately diagnosing human disease. Whenever a lot of MRIs need to be examined, traditional approach of manual tumour analysis, which relies on visual examination by a physician and radiologist, might lead to inaccurate classification. To remove human mistakes, a proposal is made for an intelligent classification system that responds to the essentials of image classification. Brain tumours are one of the primary causes of human mortality. If a tumor is diagnosed appropriately at an early stage, the chances of survival can be improved. The human brain is studied using the MRI method. The acronym MRI stands for magnetic resonance imaging. In this study, classification strategies based on Support Vector Machines (SVM) are proposed and used to brain imaging categorization. In this research, grayscale, symmetry, and texture features are utilised to extract features from MRI images. The fundamental objective of this study is to offer a decent classification result (improved accuracy and lower error rate) to detect MRI brain tumours with help of SVM. Keywords— Brain tumor, Classification, SVM, MRI.

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