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Titlebook: Classification in BioApps; Automation of Decisi Nilanjan Dey,Amira S. Ashour,Surekha Borra Book 2018 Springer International Publishing AG 2

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發(fā)表于 2025-3-21 18:57:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Classification in BioApps
副標(biāo)題Automation of Decisi
編輯Nilanjan Dey,Amira S. Ashour,Surekha Borra
視頻videohttp://file.papertrans.cn/228/227209/227209.mp4
概述Provides broad background information on and solutions to existing challenges in classifiers used for biomedical applications.Addresses various applications for the classification of biomedical signal
叢書名稱Lecture Notes in Computational Vision and Biomechanics
圖書封面Titlebook: Classification in BioApps; Automation of Decisi Nilanjan Dey,Amira S. Ashour,Surekha Borra Book 2018 Springer International Publishing AG 2
描述.This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges..Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization..In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.?.
出版日期Book 2018
關(guān)鍵詞Bio-medical signal/image analysis; Decision support systems; Machine learning; Support vector machine; F
版次1
doihttps://doi.org/10.1007/978-3-319-65981-7
isbn_softcover978-3-319-88142-3
isbn_ebook978-3-319-65981-7Series ISSN 2212-9391 Series E-ISSN 2212-9413
issn_series 2212-9391
copyrightSpringer International Publishing AG 2018
The information of publication is updating

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https://doi.org/10.1007/978-3-319-65981-7Bio-medical signal/image analysis; Decision support systems; Machine learning; Support vector machine; F
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Researching Cultures of Learning image quality is the need of the hour. The effective, yet automatic methods for measurement of quality of a medical image are of particular interest. This chapter is an overview of different medical imaging technologies, and the related image quality assessment (IQA) algorithms. The main focus is o
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Gulnissa Makhanova,Martin Cortazzic resonance images. A new method called the complex orthogonal Ripplet-II transform is proposed as a feature extraction procedure. Artificial neural network is utilized to classify the obtained features as a hemangioma or cyst. The results are evaluated with the results of the systems using Ridgelet
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Benjamin Farrand,Helena Carrapiconificance because, as has been stated frequently, the possibility of developing breast cancer is increased if the breast tissue is of high density. Radiologists predict breast tissue density by visually examining the mammogram, and the accuracy of this diagnosis is solely dependent on the experience
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