The recent advancements in the machine learning paradigm have
various applications, however, it has shown significant results in
the field of medical data analysis. The results are highly accurate
and are comparable to human experts. The various research has
proved the high accuracy of deep learning algorithms and has become
a standard choice for analysing medical data, especially medical
images, video, and electronic health records. Deep learning methods
applied to electronic health records are contributing to
understanding the evolution of chronic diseases and predicting the
risk of developing those diseases. Researchers in industry,
hospitals, and academia have published hundreds of scientific
contributions in this area during a pandemic. This book is an ideal
and relevant source of content for data science and healthcare
professionals who want to delve into complex deep learning
algorithms, calibrate models, and improve the predictions of the
trained model on medical imaging. Primary audiences for this book
are professionals and researchers in the fields of data science,
machine learning, deep learning, and AI. Also academicians,
healthcare professionals, or anyone who may have a keen interest in
how the machine and deep learning algorithms are helping in the
identification of solutions to medical sensor/image data analysis,
event detection, segmentation, and abnormality detection,
object/lesion classification, organ/region/landmark localization,
object/lesion detection, organ/substructure segmentation, lesion
segmentation, and medical image registration. The variety of
readers in the fields of government, consulting, healthcare
professionals, as well as the readers from all the social strata,
can also be benefited from this book to improve understanding of
the cutting-edge theory, technologies, methodologies, and
applications of deep Learning algorithms for medical care.
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