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Visual Computing for Industry Biomedicine and Art最新文献

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DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images dcaunet:用于颅内动脉瘤图像分割的密集卷积注意U-Net
IF 2.8 4区 计算机科学
Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-28 DOI: 10.1186/s42492-022-00105-4
Wenwen Yuan, Yanjun Peng, Yanfei Guo, Yande Ren, Qianwen Xue
{"title":"DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images","authors":"Wenwen Yuan, Yanjun Peng, Yanfei Guo, Yande Ren, Qianwen Xue","doi":"10.1186/s42492-022-00105-4","DOIUrl":"https://doi.org/10.1186/s42492-022-00105-4","url":null,"abstract":"","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45162533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Preoperative prediction of lymph node metastasis using deep learning-based features 基于深度学习特征的术前淋巴结转移预测
IF 2.8 4区 计算机科学
Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-07 DOI: 10.1186/s42492-022-00104-5
R. Cattell, Jia Ying, Lan Lei, Jie Ding, Shenglan Chen, Mario Serrano Sosa, Chuan Huang
{"title":"Preoperative prediction of lymph node metastasis using deep learning-based features","authors":"R. Cattell, Jia Ying, Lan Lei, Jie Ding, Shenglan Chen, Mario Serrano Sosa, Chuan Huang","doi":"10.1186/s42492-022-00104-5","DOIUrl":"https://doi.org/10.1186/s42492-022-00104-5","url":null,"abstract":"","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41576804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Skin lesion classification system using a K-nearest neighbor algorithm 基于k近邻算法的皮肤病变分类系统
IF 2.8 4区 计算机科学
Visual Computing for Industry Biomedicine and Art Pub Date : 2022-03-01 DOI: 10.1186/s42492-022-00103-6
Hatem, Mustafa Qays
{"title":"Skin lesion classification system using a K-nearest neighbor algorithm","authors":"Hatem, Mustafa Qays","doi":"10.1186/s42492-022-00103-6","DOIUrl":"https://doi.org/10.1186/s42492-022-00103-6","url":null,"abstract":"One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient’s history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several approaches have been developed over the years to facilitate the diagnosis based on machine learning. However, the developed systems lack certain properties, such as high accuracy. This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign. The classification process is effectuated by implementing the K-nearest neighbor (KNN) approach to differentiate between normal skin and malignant skin lesions that imply pathology. KNN is used because it is time efficient and promises highly accurate results. The accuracy of the system reached 98% in classifying skin lesions.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Publisher Correction to: Visual Computing for Industry, Biomedicine, and Art, volume 2 出版商更正:视觉计算工业，生物医学，和艺术，卷2
IF 2.8 4区 计算机科学
Visual Computing for Industry Biomedicine and Art Pub Date : 2019-09-05 DOI: 10.1186/s42492-019-0021-x
Biomedicine Visual Computing for Industry
{"title":"Publisher Correction to: Visual Computing for Industry, Biomedicine, and Art, volume 2","authors":"Biomedicine Visual Computing for Industry","doi":"10.1186/s42492-019-0021-x","DOIUrl":"https://doi.org/10.1186/s42492-019-0021-x","url":null,"abstract":"","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90797566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","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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