Deep Learning Techniques for Biomedical and Health Informatics

  • Sujata Dash
  • Biswa Ranjan Acharya
  • Mamta Mittal
  • Ajith Abraham
  • Arpad Kelemen

Part of the Studies in Big Data book series (SBD, volume 68)

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Deep Learning for Biomedical Engineering and Health Informatics

    1. Front Matter
      Pages 1-1
    2. H. B. Barathi Ganesh, U. Reshma, K. P. Soman, M. Anand Kumar
      Pages 3-21
    3. Pragatika Mishra, Sitanath Biswas, Sujata Dash
      Pages 23-40
    4. Shubham Mittal, Yasha Hasija
      Pages 57-77
    5. Jayita Saha, Chandreyee Chowdhury, Suparna Biswas
      Pages 101-126
  3. Deep Learning and Electronics Health Records

    1. Front Matter
      Pages 127-127
    2. Sujata Khedkar, Priyanka Gandhi, Gayatri Shinde, Vignesh Subramanian
      Pages 129-148
    3. Pawan Singh Gangwar, Yasha Hasija
      Pages 149-166
    4. Sagnik Sen, Rangan Das, Swaraj Dasgupta, Ujjwal Maulik
      Pages 167-186
    5. Avinash Kumar, Sobhangi Sarkar, Chittaranjan Pradhan
      Pages 211-230
    6. Jayraj Mulani, Sachin Heda, Kalpan Tumdi, Jitali Patel, Hitesh Chhinkaniwala, Jigna Patel
      Pages 231-255
  4. Deep Learning for Medical Image Processing

    1. Front Matter
      Pages 297-297
    2. G. Swapna, K. P. Soman, R. Vinayakumar
      Pages 299-327
    3. Monika Jyotiyana, Nishtha Kesswani
      Pages 329-345

About this book


This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model.

This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health.

It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.



Biomedical Engineering Health Informatics Deep Learning Machine Learning Medical Imaging Health Records Disease Prediction

Editors and affiliations

  • Sujata Dash
    • 1
  • Biswa Ranjan Acharya
    • 2
  • Mamta Mittal
    • 3
  • Ajith Abraham
    • 4
  • Arpad Kelemen
    • 5
  1. 1.Department of Computer ScienceNorth Orissa UniversityTakatpurIndia
  2. 2.School of Computer Science and EngineeringKIIT Deemed to UniversityBhubaneswarIndia
  3. 3.Computer Science and Engineering DepartmentG. B. Pant Government Engineering CollegeNew DelhiIndia
  4. 4.Scientific Network for Innovation and Research ExcellenceMachine Intelligence Research LabsAuburnUSA
  5. 5.Department of Organizational Systems and Adult HealthUniversity of MarylandBaltimoreUSA

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