Pub Date : 2023-05-16DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-16DOI: 10.1080/13682199.2023.2187514
B. Veerasamy, B. Bharathi, A. Ahilan
ABSTRACT 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.
{"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":null,"pages":null},"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}
Pub Date : 2023-05-16DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-15DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-13DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-13DOI: 10.1080/13682199.2023.2210402
K. G. Satheesh Kumar, V. Arunachalam
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.)
2019年全球大流行的冠状病毒病(COVID-19)会导致人类严重的呼吸道问题。胸部x线(CXR)成像技术主要有助于发现由COVID-19引起的胸部和肺部异常。因此，开发基于cxr的COVID-19自动检测系统对于疾病诊断至关重要。为了实现这一要求，本文提出了一种增强的残差网络(ResNet)模型，用于准确检测COVID-19。该模型结合深度可分卷积ResNet和金字塔扩展模块(DSC-ResNet-PDM)进行深度特征提取。采用DSC层减少了参数的数量，以减轻过拟合问题。进一步，利用金字塔扩展模块提取多尺度特征。最后将提取的特征输入到优化的中高斯核支持向量机分类器(MGKSVM)中进行COVID-19检测。该模型的准确率为99.5%，相对于ResNet50和ResNet101标准模型有所提高。image Science Journal版权归Taylor & Francis Ltd所有，未经版权所有者明确书面许可，不得将其内容复制或通过电子邮件发送到多个网站或发布到listserv。但是，用户可以打印、下载或通过电子邮件发送文章供个人使用。这可以删节。对副本的准确性不作任何保证。用户应参阅原始出版版本的材料的完整。(版权适用于所有人。)
{"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":null,"pages":null},"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}
Pub Date : 2023-05-11DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-11DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-08DOI: 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":null,"pages":null},"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}
Pub Date : 2023-05-07DOI: 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":null,"pages":null},"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}
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