Despite the remarkable progress witnessed in the last decade in big data utilization and parallel processing techniques, a persistent disparity exists between the capabilities of computer-aided diagnosis systems and the intricacies of practical healthcare scenarios. This disconnection is particularly evident in the complex landscape of artificial intelligence (AI) and IoT innovations within the biomedical realm. The need to bridge this gap and explore the untapped potential in healthcare and biomedical applications has never been more crucial. As we navigate through these challenges, Applications of Parallel Data Processing for Biomedical Imaging offers insights and solutions to reshape the future of biomedical research.

The objective of Applications of Parallel Data Processing for Biomedical Imaging is to bring together researchers from both the computer science and biomedical research communities. By showcasing state-of-the-art deep learning and large data analysis technologies, the book provides a platform for the cross-pollination of ideas between AI-based and traditional methodologies. The collaborative effort seeks to have a substantial impact on data mining, AI, computer vision, biomedical research, healthcare engineering, and other related fields. This interdisciplinary approach positions the book as a cornerstone for scholars, professors, and professionals working in software and medical fields, catering to both graduate and undergraduate students eager to explore the evolving landscape of parallel computing, artificial intelligence, and their applications in biomedical research.

The topics covered in the book span a diverse range, including heterogeneous computing, biological and molecular computing, AI applications in biomedical imaging, big data processing, and future network architectures for parallel processing. Readers will delve into the frontiers of AI and deep learning in medicine, human biology, and healthcare, exploring machine learning and deep learning-based clinical decision-making systems, biomedical imaging, medical and healthcare education, and more. The book acts as a beacon for those intrigued by the possibilities at the intersection of big data, intelligent IoT, and parallel computing in reshaping the landscape of healthcare, bioinformatics, biomechanics, and biomedical services.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

AI In Big Data Processing
AI In Biomedical Imaging Applications
AI In Healthcare And Biomedical Imaging
Big Data Analytics For Healthcare
Big Data In Biomedical Services
Computer Vision For Medical Images
Deep Learning In Biomedical Imaging
Distributed Systems In Healthcare
Future Network Architectures For Parallel Processing
Heterogeneous Computing
Intelligent Devices For Healthcare Services
IoT For Smart Healthcare
Molecular Computing
Process-Aware Information Systems In Healthcare
Smart Computing In Bioinformatics

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Editor/Author Biographies

Rijwan Khan received his B. Tech Degree in Computer Science & Engineering from BIT, M. Tech in Computer Science & Engineering from IETE, and Ph.D.in Computer Engineering from Jamia Millia Islamia, New Delhi. He has 17 years of Academic & Research Experience in various areas of Computer Science. He is currently working as Professor in the Department Computer Science and Engineering at Galgotias University, G. B. Nagar, U.P. He is author of three subject books. He published more than 460 research papers in different journals and conferences. He has been nominated in the board of reviewers of various peer-reviewed and refereed Journals. He is Editor of five research books. He is Editor of seven special issues of SCI and Scopus indexed journals. He was session chaired in three International conferences & Keynote speakers in some the national and International conferences

Indrajeet Kumar received his B Tech in Computer Science & Engineering from Sant Longowal Institute of Engineering and Technology, Punjab in 2009 and M Tech in Computer Engineering with specialization in Computer Network from YMCA University of Science & technology, Faridabad in 2013. He received his PhD on Analysis and Classification of Breast density classification using Mammographic images from GB Pant Institute of Engineering & Technology, Ghurdauri,Uttarakhand, India in 2019. During his PhD he worked on enhancing the potential of most commonly available Breast density classification for differential diagnosis between atypical cases of focal density cases. He served in academia in various reputed organizations like Galgotias Institutions, Greater Noida, U.P, India and graphic era hill University, Dehradun, India, for 6 years. His research interests include application of machine learning, Deep Learning and soft computing techniques for analysis of medical images.

Pushkar Praveen ”The only limit to our realization of tomorrow will be our doubts of today” by Franklin D. Roosevelt. The only thing standing between me and my goals is my own self-doubt. By believing in myself and taking action toward my dreams, I can achieve anything I set my mind to. My passion for technology started early in my academic career, and I have spent countless hours honing my skills and knowledge. I have taken online courses, attended seminars, and worked on personal projects to build a solid foundation. I am a quick learner, and a good team player and I enjoy working on challenging projects that push me to learn and grow. I am confident that my drive and enthusiasm, coupled with my technical abilities, make me a valuable asset to any organization. I am actively seeking opportunities to start my career in the IT field, and I am excited about the possibilities that lie ahead. If you are looking for a dedicated and motivated team member, please feel free to connect with me.

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