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Deep Learning Architectures for Medical Diagnosis

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

Author

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  • Vishakha Malik

    (Vellore Institute of Technology)

  • S. Maheswari

    (Vellore Institute of Technology)

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.

Suggested Citation

  • Vishakha Malik & S. Maheswari, 2020. "Deep Learning Architectures for Medical Diagnosis," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1569-1577, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_161
    DOI: 10.1007/978-3-030-41862-5_161
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