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K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI 基于K-Net-Deep关节分割与Taylor驾驶训练优化的深度学习脑肿瘤MRI分类
The Imaging Science Journal Pub Date : 2023-05-16 DOI: 10.1080/13682199.2023.2208963
V. Prasad, Vairamuthu S, Selva Rani B
{"title":"K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI","authors":"V. Prasad, Vairamuthu S, Selva Rani B","doi":"10.1080/13682199.2023.2208963","DOIUrl":"https://doi.org/10.1080/13682199.2023.2208963","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76452403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Video compression using improved diamond search hybrid teaching and learning-based optimization model 基于改进菱形搜索的视频压缩混合教与学优化模型
The Imaging Science Journal Pub Date : 2023-05-16 DOI: 10.1080/13682199.2023.2187514
B. Veerasamy, B. Bharathi, A. Ahilan
{"title":"Video compression using improved diamond search hybrid teaching and learning-based optimization model","authors":"B. Veerasamy, B. Bharathi, A. Ahilan","doi":"10.1080/13682199.2023.2187514","DOIUrl":"https://doi.org/10.1080/13682199.2023.2187514","url":null,"abstract":"ABSTRACT\u0000 Video compression is necessary to recreate a video without sacrificing quality. Nowadays, researchers are focusing on global optimization approaches to determine the optical flow of the neighboring pixels in video processing. In this work, a novel improved diamond search-hybrid teaching-learning based optimization (IDS-HTLBO) methodology has been proposed to compress the videos and increase the video quality. This method uses a diamond search pattern with a secure number of search points for per frame of the video. The hybridization of DS algorithm and TLBO algorithm are applied in this methodology to reduce computational complexity. Moreover, this method reduces the computational unpredictability of block matching. The quality of the image was validated with 3D reconstruction by the structured light approaches. The experimental result shows that the proposed IDS-HTLBO algorithm achieves a maximum average value of 53.17 dB, 0.44 and 11.57 in terms of peak-to-signal-noise ratio, mean squared error, and compression ratio respectively.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"19 1","pages":"573 - 584"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82804587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity 一种先进的模糊c均值算法用于存在噪声和强度不均匀性的脑磁共振图像的组织分割
The Imaging Science Journal Pub Date : 2023-05-16 DOI: 10.1080/13682199.2023.2210400
Sandhya Gudise, K. Giri Babu, T. Satya Savithri
{"title":"An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity","authors":"Sandhya Gudise, K. Giri Babu, T. Satya Savithri","doi":"10.1080/13682199.2023.2210400","DOIUrl":"https://doi.org/10.1080/13682199.2023.2210400","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90328769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Skull stripping on multimodal brain MRI scans using thresholding and morphology 颅骨剥离的多模态脑MRI扫描使用阈值和形态学
The Imaging Science Journal Pub Date : 2023-05-15 DOI: 10.1080/13682199.2023.2208923
S. Y. Bhat, Afnan Naqshbandi, M. Abulaish
{"title":"Skull stripping on multimodal brain MRI scans using thresholding and morphology","authors":"S. Y. Bhat, Afnan Naqshbandi, M. Abulaish","doi":"10.1080/13682199.2023.2208923","DOIUrl":"https://doi.org/10.1080/13682199.2023.2208923","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77299533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
SHBO-based U-Net for image segmentation and FSHBO-enabled DBN for classification using hyperspectral image 基于shbo的U-Net用于图像分割，基于fshbo的DBN用于高光谱图像分类
The Imaging Science Journal Pub Date : 2023-05-13 DOI: 10.1080/13682199.2023.2208927
T. Subba Reddy, V. Krishna Reddy, R. Vijaya Kumar Reddy, Dr. Chandra Sekhar Kolli, V. Sitharamulu, M. Chandrababu
{"title":"SHBO-based U-Net for image segmentation and FSHBO-enabled DBN for classification using hyperspectral image","authors":"T. Subba Reddy, V. Krishna Reddy, R. Vijaya Kumar Reddy, Dr. Chandra Sekhar Kolli, V. Sitharamulu, M. Chandrababu","doi":"10.1080/13682199.2023.2208927","DOIUrl":"https://doi.org/10.1080/13682199.2023.2208927","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85515729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images 基于深度卷积的金字塔ResNet模型在胸部x线图像中准确检测COVID-19
The Imaging Science Journal Pub Date : 2023-05-13 DOI: 10.1080/13682199.2023.2210402
K. G. Satheesh Kumar, V. Arunachalam
{"title":"Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images","authors":"K. G. Satheesh Kumar, V. Arunachalam","doi":"10.1080/13682199.2023.2210402","DOIUrl":"https://doi.org/10.1080/13682199.2023.2210402","url":null,"abstract":"The global pandemic of coronavirus disease 2019 (COVID-19) causes severe respiratory problems in humans. The Chest X-ray (CXR) imaging technique majorly assists in detecting abnormalities in the chest and lung areas caused by COVID-19. Hence, developing an automatic system for CXR-based COVID-19 detection is vital for disease diagnosis. To accomplish this requirement, an enhanced Residual Network (ResNet) model is proposed in this paper for accurate COVID-19 detection. The proposed model combines the Depthwise Separable Convolutional ResNet and Pyramid dilated module(DSC-ResNet-PDM) for deep feature extraction. Employing the DSC layer minimizes the number of parameters to mitigate the overfitting issue. Further, the pyramid dilated module is used for extracting multi-scale features. The extracted features are finally fed into the optimized Medium Gaussian kernel Support Vector Machine classifier (MGKSVM) for COVID-19 detection. The proposed model attained an accuracy of 99.5%, which is comparatively higher than the standard ResNet50 and ResNet101 models. [ FROM AUTHOR] Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91429046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep remote fusion: development of improved deep CNN with atrous convolution-based remote sensing image fusion 深度遥感融合:基于亚历克斯卷积的遥感图像融合改进深度CNN的发展
The Imaging Science Journal Pub Date : 2023-05-11 DOI: 10.1080/13682199.2023.2206761
S. Nagarathinam, A. Vasuki, K. Paramasivam
{"title":"Deep remote fusion: development of improved deep CNN with atrous convolution-based remote sensing image fusion","authors":"S. Nagarathinam, A. Vasuki, K. Paramasivam","doi":"10.1080/13682199.2023.2206761","DOIUrl":"https://doi.org/10.1080/13682199.2023.2206761","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88824304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An efficient brain tumor classification using MRI images with hybrid deep intelligence model 基于混合深度智能模型的MRI图像高效脑肿瘤分类
The Imaging Science Journal Pub Date : 2023-05-11 DOI: 10.1080/13682199.2023.2207892
A. V. Reddy, P. Mallick, B. Srinivasa Rao, Phaneendra Kanakamedala
{"title":"An efficient brain tumor classification using MRI images with hybrid deep intelligence model","authors":"A. V. Reddy, P. Mallick, B. Srinivasa Rao, Phaneendra Kanakamedala","doi":"10.1080/13682199.2023.2207892","DOIUrl":"https://doi.org/10.1080/13682199.2023.2207892","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79150326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Opponent colour median-based scale invariant local ternary pattern: a new feature descriptor for moving object detection 基于对手颜色中值的尺度不变局部三元模式:一种新的运动目标检测特征描述符
The Imaging Science Journal Pub Date : 2023-05-08 DOI: 10.1080/13682199.2023.2207887
K. Kalirajan, K. Balaji
{"title":"Opponent colour median-based scale invariant local ternary pattern: a new feature descriptor for moving object detection","authors":"K. Kalirajan, K. Balaji","doi":"10.1080/13682199.2023.2207887","DOIUrl":"https://doi.org/10.1080/13682199.2023.2207887","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75927551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Brain tumor segmentation and classification using optimized U-Net 基于优化U-Net的脑肿瘤分割与分类
The Imaging Science Journal Pub Date : 2023-05-07 DOI: 10.1080/13682199.2023.2200614
S. K V
{"title":"Brain tumor segmentation and classification using optimized U-Net","authors":"S. K V","doi":"10.1080/13682199.2023.2200614","DOIUrl":"https://doi.org/10.1080/13682199.2023.2200614","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82120439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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