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Titlebook: Deep Learning Applications in Image Analysis; Sanjiban Sekhar Roy,Ching-Hsien Hsu,Venkateshwara Book 2023 The Editor(s) (if applicable) a

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發(fā)表于 2025-3-21 19:28:11 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Deep Learning Applications in Image Analysis
編輯Sanjiban Sekhar Roy,Ching-Hsien Hsu,Venkateshwara
視頻videohttp://file.papertrans.cn/265/264566/264566.mp4
概述Reviews exhaustively the key recent research into deep learning applications in image analysis.Covers many different deep learning applications in medical, satellite, forensic image analysis.Demonstra
叢書(shū)名稱(chēng)Studies in Big Data
圖書(shū)封面Titlebook: Deep Learning Applications in Image Analysis;  Sanjiban Sekhar Roy,Ching-Hsien Hsu,Venkateshwara  Book 2023 The Editor(s) (if applicable) a
描述This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3..The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and
出版日期Book 2023
關(guān)鍵詞Deep Learning; Image Processing; Medical Image Processing; Satellite Image Classification; Convolutional
版次1
doihttps://doi.org/10.1007/978-981-99-3784-4
isbn_softcover978-981-99-3786-8
isbn_ebook978-981-99-3784-4Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Deep Learning-Based Approaches Using Feature Selection Methods for Automatic Diagnosis of COVID-19 nents and hundreds of countries, will go down in history as the first pandemic produced by coronaviruses. The World Health Organization (WHO) classified COVID-19 as a “pandemic” on March 11, 2020. To avoid the future spread of this pandemic and to promptly treat infected patients, it is crucial to f
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Plant Diseases Classification Using Neural Network: AlexNet,lution and not even budget friendly. To provide all the farmers and cultivators with smartphones with internet access, we could reduce the food loss in the country. In this chapter we have covered how deep learning can be used to make an image classifier based on AlexNet. The trained weight is then
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Hyperspectral Images: A Succinct Analytical Deep Learning Study,lications to global safety issues with core concepts of classification, segmentation, anomaly detection and prediction. The use of DL on satellite images and to achieve best performance, the research is swiftly trending from traditional machine learning to deep learning approaches..In this chapter,
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Deep Learning-Based Approaches Using Feature Selection Methods for Automatic Diagnosis of COVID-19 AY images and reduce the number of house gray tones The application of artificial intelligence and machine learning techniques to radiological images helps to detect this disease accurately and quickly. In this study, 13 different deep learning techniques were studied using Chi-square, NCA, mRMR and
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