Preface

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.