Articles liés à Computer-Assisted Analysis for Digital Medicinal Imagery
Synopsis
The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge lies-a need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes.
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À propos des auteurs
Amit Sinha is currently working as a Professor and Dean (T&P) at ABES Engineering College, Ghaziabad. He is also handling Dean (T&P). Dr. Sinha has done MTech and PhD in Computer Science & Engineering and has more than 24 years of teaching experience. He has 22 publications (5-SCI, 8-Scopus indexed and 11 in Scopus indexed Conferences). He has authored 5 books, 10 patents (8 patents published & 2 patents granted) and 5 book chapters in Scopus indexed book series. Dr Sinha has received funding from government agencies for two projects and 8 FDPs, including ATAL FDP. He is guiding 6 PhD scholars with one awarded scholar. Dr. Sinha is a CMI Level 5 certified Professional under the AICTE-UKIERI program. He is honoured as a Chartered Engineer of IE India and a Senior Member of IEEE UP Section, India. Dr. Amit Sinha has organized two international conferences and is a reviewer of various journals.
Pranshu Saxena received the B. E. in (2010), M. Tech. (2013), and PhD in 2023. He is currently an Assistant Professor in the Department of Information Technology at the ABES Engineering College, Ghaziabad, India. He has published 22 research articles which include 4-SCI, 6-Scopus, and 12 peer-review journals/conferences. His research interests include medical image processing, automated image segmentation, Image texture analysis, intelligent systems and Pattern Classification. He has been a designated reviewer (ISIEA 2012-2013, IJERT) and committee member (IJACR) for many international conferences and journals in image processing and medical imaging. He has published one book published by LAP Lambert Academic Publishing, USA.
Sanjay Kumar Singh is currently working as an assistant professor in university school of automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi campus, Delhi. He has more than 12 years of experience in teaching and received integrated post graduate (B. Tech + M. Tech) degree from ABV-Indian Institute of Information Technology & Management, Gwalior and Ph. D. degree from IKG-Punjab Technical University. He has published more than 50 papers in reputed journals and international conferences. His area of research in the field of machine learning, deep learning, soft computing and intelligent systems.
Harikesh Singh is a Senior IEEE Member and working as Associate Professor in Department of Computer Science and Engineering at JSS Academy of Technical Education, Noida with 17 years of teaching experience since 1st Jul, 2022. He has done BTech in 2004, ME in 2007, and PhD in 2015. Earlier, he has served as Assistant Professor in CSE Department at Jaypee University of Engineering and Technology, Guna, Madhya Pradesh for 12 Years and afterwards, he worked at ABES Engineering College, Ghaziabad for 3 years as Associate Professor in IT Department. He has supervised 4 MTech Students and 1 PhD Student in the area of Grid and Cloud Computing. He has published 7 SCI, and 22 Scopus-indexed journal and conference papers till date. Currently, he is working in the field of Machine Learning and AI with IoT and Blockchain Technology. He has 10 published patents and organised many faculty development programs.
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Paperback. Etat : new. Paperback. The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge liesa need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798369352274
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Paperback. Etat : new. Paperback. The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge liesa need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798369352274
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Paperback. Etat : new. Paperback. The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge liesa need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798369352274
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