Druck auf Anfrage Neuware - Printed after ordering - This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

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Detalles bibliogr�ficos

T�tulo: Deep Learning and Computer Vision: Models and Biomedical Applications : Volume 2
Editor: Springer Nature Singapore, Springer Nature Singapore
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 9819636477
ISBN 13: 9789819636471
Encuadernaci�n: Buch
Estado: Neu

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Sinopsis

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

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Dr. Uma N. Dulhare is currently working as a Professor & Head Computer Science &Artificial Intelligence Department, MuffaKham Jah College of Engineering & Technology, Hyderabad, India. She has more than 20 years of teaching experience. She received her Ph.D. degree in Computer Science from Osmania University, Hyderabad. Her research interests include Data Mining, Big Data Analytics, and Machine Learning, IoT, Evolutionary Computing, Biomedical Image Processing. She has published more than 40 research papers in prestigious National, International Journals & book chapters. She is a member of the editorial board for various National and International journals in the field of Computer Science and program committee member/reviewer for various International conferences/Journals such as Elsevier, Springer, MDPI, Multimedia Tools & Applications & also chaired the sessions at various International conferences.

Essam H. Houssein (Member, IEEE) received Ph.D. degree in computer science, in 2012. He is currently a Professor of Artificial Intelligence at the Faculty of Computers and Information, Minia University, Minia, Egypt. He is the founder and chair of the Artificial Intelligence Research (AIR) Group, Egypt. He is selected as a Highly Cited Researcher 2023, in 2024 Edition of the Ranking of Top Scientists in the field of Computer Science. He has published more than 240 scientific research articles in prestigious international journals. His research interests include Meta-heuristics Optimization Algorithms, Artificial Intelligence, WSN, Bioinformatics, Internet of Things, Artificial Intelligence, Image Processing, and Data Mining. He serves as a reviewer for more than 120 journals, such as Elsevier, Springer, and IEEE.

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