Image Data Classification Using Mobile Net for Big Data Analytics

Voruganti Ajay Krishna, D. Nagajyothi, AtthapuramAkshay Reddy, D. Aravind
{"title":"Image Data Classification Using Mobile Net for Big Data Analytics","authors":"Voruganti Ajay Krishna, D. Nagajyothi, AtthapuramAkshay Reddy, D. Aravind","doi":"10.1109/icdcece53908.2022.9792904","DOIUrl":null,"url":null,"abstract":"Deep learning has more advantages than machine learning approaches, with applications including picture classification, image analysis, clinical archives, and beholding. The archives of medical photographs are developing tremendously as a result of hospitals' extensive use of digital images as data. Digital images play an important part in forecasting the severity of a patient's condition, and medical images are widely used in identification and inquiry. Because of recent advancements in imaging technology, identifying medical images in a automatic manner is an open Research Problem for computer vision experts. A best suited most significant for classifying medical images in accordance with their relevant categories. It has been a propensity to provide a model in which an algorithmic program is taught for identifying medical related images using Deep Learning techniques. A pre-trained DCNN Google net and Mobile net are used to classify a variety of medical images for various body parts, and the models are compared. This image categorization technique is useful for predicting the acceptable class or classes of random unfamiliar images. The experiment's results show that proposed method will be best suited to classify a wide range of medical images.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icdcece53908.2022.9792904","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Deep learning has more advantages than machine learning approaches, with applications including picture classification, image analysis, clinical archives, and beholding. The archives of medical photographs are developing tremendously as a result of hospitals' extensive use of digital images as data. Digital images play an important part in forecasting the severity of a patient's condition, and medical images are widely used in identification and inquiry. Because of recent advancements in imaging technology, identifying medical images in a automatic manner is an open Research Problem for computer vision experts. A best suited most significant for classifying medical images in accordance with their relevant categories. It has been a propensity to provide a model in which an algorithmic program is taught for identifying medical related images using Deep Learning techniques. A pre-trained DCNN Google net and Mobile net are used to classify a variety of medical images for various body parts, and the models are compared. This image categorization technique is useful for predicting the acceptable class or classes of random unfamiliar images. The experiment's results show that proposed method will be best suited to classify a wide range of medical images.
利用移动网络进行大数据分析的图像数据分类
深度学习比机器学习方法有更多的优势,其应用包括图片分类、图像分析、临床档案和观察。由于医院广泛使用数字图像作为数据,医学照片档案正在极大地发展。数字图像在预测患者病情严重程度方面发挥着重要作用,医学图像在识别和查询方面得到广泛应用。由于近年来成像技术的进步,自动识别医学图像是计算机视觉专家的一个开放研究问题。一个最适合最显著的分类医学图像按照他们的相关类别。它一直倾向于提供一个模型,在这个模型中,一个算法程序被教授用于使用深度学习技术识别医学相关图像。使用预训练好的DCNN Google网和Mobile网对不同身体部位的多种医学图像进行分类,并对模型进行比较。这种图像分类技术对于预测随机不熟悉图像的可接受类别或类别非常有用。实验结果表明,该方法最适合于对大范围的医学图像进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信