Advertisement

Thyroid Diagnosis Using Multilayer Perceptron

  • B. Nageshwar RaoEmail author
  • D. Laxmi Srinivasa ReddyEmail author
  • G. BhaskarEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Thyroid disease is one of main origin of serious medical issues for human subsistence. Therefore, proper diagnosis of thyroid disease is treated as an important issue to determine treatment for patients. A new approach on Multi-layer Perception (MLP) using back propagation learning algorithm to classify Thyroid disease is presented. It consists of an input layer with 4 neurons, 10 hidden layer with 3 neurons and an output layer with just 1 neuron. The relevant choice of activation objective and the number of neurons in the hidden layer and also the number of layers are achieved using MLP test and error method. The proposed method shows better performance in terms of classification accuracy. For simulation results MATLAB Tool is used.

Keywords

Thyroid Multi-layer Perception (MLP) Activation function Artificial neural networks 

References

  1. 1.
    RamakrishnaMurty M, Murthy JVR, Prasad Reddy PVGD (2011) Text document classification based on a least square support vector machines with singular value decomposition. Int J Comput Appl (IJCA) indexed by DOAJ, Informatics, ProQuest CSA research database, NASA ADS (Hardward university)etc, ISBN 978-93-80864-56-6,  https://doi.org/10.5120/3312-4540, [impact factor 0.821, 2012] 27(7):21–26CrossRefGoogle Scholar
  2. 2.
    Himabindu G, Ramakrishna Murty M et al (2018) Classification of kidney lesions using bee swarm optimization. Int J Eng Technology 7(2.33):1046–1052Google Scholar
  3. 3.
    Himabindu G, Ramakrishna Murty M et al (2018) Extraction of texture features and classification of renal masses from kidney images. Int J Eng Technology 79(2.33):1057–1063Google Scholar
  4. 4.
    Navya M, Ramakrishna Murty M et al (2018) A comparative analysis of breast cancer data set using different classification methods. International Conference and published the proceedings in AISC, Springer, SCI-2018 Google Scholar
  5. 5.
    Lederman D (2002) Automatic classification of infants cry. M.Sc. Thesis, Department of Electrical and Computer Engineering: Ben-Gurion University of The Negev. Negev, IsraelGoogle Scholar
  6. 6.
    Ham FM, Kostanic I (2001) Principles of neurocomputing for science and engineering. McGraw Hill, New YorkGoogle Scholar
  7. 7.
    Protopapasa V, Eimas, PD (1997) Perceptual differences in infant cries revealed by modifications of acoustic features. Acoust Soc Am 102:3723–3734Google Scholar
  8. 8.
    Dey R, Bajpai V, Gandhi G, Dey B (2008) Application of artificial neural network (ANN) technique for diagnosing diabetes mellitus. In: IEEE third international Conference on Industrial and Information Systems (ICIIS) Kharagpur, India, pp 1–4Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and Information TechnologyOsmania UniversityHyderabadIndia
  2. 2.Department of MCACBIT-HyderabadHyderabadIndia
  3. 3.Electronics and Communication EngineeringOsmania UniversityHyderabadIndia

Personalised recommendations