Editors: Parma Nand, Vishal Jain, Dac-Nhuong Le, Jyotir Moy Chatterjee, Ramani Kannan, Abhishek S. Verma

Series Title: IoT and Big Data Analytics

Deep Learning for Healthcare Services

Volume 2

eBook: US $39 Special Offer (PDF + Printed Copy): US $63
Printed Copy: US $43
Library License: US $156
ISSN: 2972-4147 (Print)
ISSN: 2972-4155 (Online)
ISBN: 978-981-5080-24-7 (Print)
ISBN: 978-981-5080-23-0 (Online)
Year of Publication: 2023
DOI: 10.2174/97898150802301230201


This book highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include:

- The Role of Deep Learning in Healthcare Industry: Limitations

- Generative Adversarial Networks for Deep Learning in Healthcare

- The Role of Blockchain in the Healthcare Sector

- Brain Tumor Detection Based on Different Deep Neural Networks

Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening.

Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.


Health care industry professionals, scholars in computer science and health care management


This book aims to highlight the different applications of deep learning algorithms in implementing Big Data and IoT-enabled smart solutions to treat and care for terminally ill patients. The book shall also unveil how the combination of big data, IoT, and the cloud can empower the conventional doctor-patient relationship in a more dynamic, transparent, and personalized manner. Incorporation of these smart technologies can also successfully port over powerful analytical methods from the financial services and consumer industries like claims management. This coupled with the availability of data on social determinants of health – such as socioeconomic status, education, living status, and social networks – opens novel opportunities for providers to understand individual patients on a much deeper level, opening the door for precision medicine to become a reality. The real value of such systems stems from their ability to deliver in-the-moment insights to enable personalized care, understand variations in care patterns, risk-stratify patient populations, and power dynamic care journey management and optimization. Successful application of deep learning frameworks to enable meaningful, cost-effective personalized healthcare services is the primary aim of the healthcare industry in the present scenario. However, realizing this goal requires effective understanding, application, and amalgamation of deep learning, IoT, Big Data, and several other computing technologies to deploy such systems effectively. This book shall help clarify understanding of certain key mechanisms and technologies helpful in realizing such systems. Through this book, we attempt to combine numerous compelling views, guidelines, and frameworks on enabling personalized healthcare service options through the successful application of Deep Learning frameworks.

Chapter 1 represents a survey of the role of deep learning in the healthcare industry with its challenges and future scope.

Chapter 2 focuses on recent work done in GAN and implements this technique in the different deep-learning applications for healthcare.

Chapter 3 focuses on the role of blockchain in biomedical engineering applications.

Chapter 4 compares three different architectures of Convolutional Neural Networks (CNN), VGG16, and ResNet50, and visually represents the result to the users using a GUI.

Chapters 5 propose an efficient model for medical image contrast enhancement and correct tumor prediction.

Parma Nand
School of Engineering and Technology
Sharda University Greater Noida, U.P.

Vishal Jain
Department of Computer Science and Engineering
School of Engineering and Technology
Sharda University, Greater Noida
U.P., India

Dac-Nhuong Le
Faculty of Information Technology
Haiphong University, Haiphong

Jyotir Moy Chatterjee
Lord Buddha Education Foundation
Kathmandu, Nepal

Ramani Kannan
Center for Smart Grid Energy Research
Institute of Autonomous System
Universiti Teknologi PETRONAS (UTP)


Abhishek S. Verma
Department of Computer Science & Engineering
School of Engineering & Technology
Sharda University, Greater Noida
U.P., India