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Titlebook: Advanced Machine Learning Approaches in Cancer Prognosis; Challenges and Appli Janmenjoy Nayak,Margarita N. Favorskaya,Manohar Mi Book 2021

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發(fā)表于 2025-3-21 18:39:55 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advanced Machine Learning Approaches in Cancer Prognosis
期刊簡(jiǎn)稱Challenges and Appli
影響因子2023Janmenjoy Nayak,Margarita N. Favorskaya,Manohar Mi
視頻videohttp://file.papertrans.cn/146/145838/145838.mp4
發(fā)行地址Discusses all types of cancer diseases information with their detection, solution, and prevention.Presents advanced machine learning approaches spanning the areas of neural networks, fuzzy logic, conn
學(xué)科分類Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Advanced Machine Learning Approaches in Cancer Prognosis; Challenges and Appli Janmenjoy Nayak,Margarita N. Favorskaya,Manohar Mi Book 2021
影響因子.This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic,?and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning?have?proved its vast range of significance and,?provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D.?students, postdocs, and anyone interested in the subjects discussed. ?.
Pindex Book 2021
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Lecture Notes in Computer Sciencesing short-breaths will be the ease of usage and compatibility of the network. The prediction can lead to earliest diagnosis possible when the concerned person identifies unusual breathing habits. The prediction can also propose other tests to be done if required. The creation of networks for both w
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Lecture Notes in Computer Scienced pathological observations of the thyroid disease were also surveyed. The forty-two machine learning algorithms are compared to find the top five best classifiers to predict whether a given patient is suffering from hypothyroidism, hyperthyroidism or is absolute normal. The data source has been tak
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Bryan Pauken,Mudit Pradyumn,Nasseh Tabriziared with large collection of databases with the replacement of sigmoid activation function. Probabilistic neural network is used to describe nonlinear statement limits which further leads to Bayes optimal and also all the function which bear same properties as well. Any input data or algorithm can
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https://doi.org/10.1007/978-3-030-96282-1G16, EfficientNet, Dense Net121, ResNext50 in the large-scale cancer image data classification setting. Our main contribution is to focus on the high-level accuracy because these deep learning algorithms have the capability of transfer learning with image instant segmentation.
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