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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Deep Learning Architectures for Medical Diagnosis

verfasst von : Vishakha Malik, S. Maheswari

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

Verlag: 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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Vorheriges Kapitel Local Painted Texture Pattern for Quality of Content Based Image Retrieval
Nächstes Kapitel Improved Blog Classification Using Multi Stage Dimensionality Reduction Technique
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Metadaten
Titel
Deep Learning Architectures for Medical Diagnosis
verfasst von
Vishakha Malik
S. Maheswari
Copyright-Jahr
2020
Verlag
Springer International Publishing
Buch
New Trends in Computational Vision and Bio-inspired Computing

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

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

Copyright-Jahr: 2020

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

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