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2020 | OriginalPaper | Chapter

Deep Learning Architectures for Medical Diagnosis

Authors : Vishakha Malik, S. Maheswari

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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Abstract

The medical science is practising on the determination, treatment and aversion of the different disease. An AD is a type of neural dementia that causes a human body-brain especially memory, cognitive skills, and other parts of the brain. The motivation behind this examination is to propose an efficient algorithm structure using deep learning architectures methods and techniques for the perception of Alzheimer disease. In this study, the deep learning architecture structure is created using data normalization, generalized linear neural network (GLNN), regression techniques (softmax), K-means clustering. The detection of Alzheimer’s is done using the combined dataset of the spinal cord and brain. Compared to the previous workflows these methods are capable of detecting the Alzheimer at the minimal timestamp.

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Metadata
Title
Deep Learning Architectures for Medical Diagnosis
Authors
Vishakha Malik
S. Maheswari
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_161

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