Page 9 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

xvi Preface

advanced algorithms. Some algorithms are best suited to
perform specific tasks. In order to choose the right ones, it is
good to gain grasp of all primary algorithms. Excellent knowl-
edge of advanced deep learning techniques, their types, and
applications can help users execute them for various purposes.
Chapter 4 aims at critically analyzing the existing techniques
used in blockchains and their suitability in education domain.
The advantages and challenges in using blockchain-based ap-
plications in education are also discussed in this chapter. Secu-
rity breaches and attacks on using blockchains are discussed
along with possible countermeasures. A plan of how existing
models can be improved to enhance performance of blockchains
in applications belonging to education is also discussed.
Chapter 5 investigates the use of different deep neural
network architectures and natural language processing for
depression detection in cancer communities. Depression
detection using sentiment affect can be of great assistance to the
doctors treating cancer patients and aid them in deciding
whether along with the cancer treatment their patients need help
from psychologists or psychiatrists.
Chapter 6 focuses on early disease recognition that requires
high-resolution images. After a preprocessing step using a
contrast enhancement method, all the diseased blobs are
segmented for the whole dataset. A list of several measurement-
based features that represent the blobs is chosen and selected
based on principle component analysis. The features are used as
inputs for a standard feedforward neural network. Our results
show competitive classification results not only with other deep
learning approaches, such as CNNs, but also with a simpler
network structure.
Chapter 7 determines how deep learning helps in the early
diagnosis of several diseases such as Alzheimer’s disease, rheu-
matic diseases, autism spectrum disorder, and more. After
expanding upon the basics of deep learning and biomedical
engineering, the chapter explores more upon diagnostics using
deep learning and discusses the early diagnosis of certain
diseases.
Chapter 8 details on the advancement in the subset of ma-
chine learning; the deep learning made this research area into
high potential in terms of precise prediction and accuracy. Many
versions of deep learningebased architecture are implemented
along with various nonvisualization and visualization techniques