Hybrid Auto Encoder for Dimensionality Reduction in Ct Scan Images of Heart

Authors

  • Jayaram M. Professor, Department of CSE(Data Science), Sreyas Institute of Engineering & Technology, Hyderabad, India
  • J. Lakshmi Narayana Professor , Department of ECE, PSCMR College Of Engineering and Technology, Vijayawada, A.P.
  • A. Pathanjali Sastri Professor, Department of CSE, PSCMR College Of Engineering and Technology, Vijayawada, A.P
  • Santhi Sri. T. Professor, Department of Computer Science and Engineering, Koneru lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh.

Keywords:

De-noising Encoder, Convolution Neural Network, Sensors, Decoder, Identity Function

Abstract

Modern style of food habit system and stress life has completely impacted the human life. Sudden deaths irrespective of age have become most common now days. In order to save human life, predict heart attack system many of the existing systems are working on the textual attributes but in the proposed model, the major focus of the heart attack prediction is based on the CT SCAN, continuous monitoring of which helps the doctors to diagnosis the patients effectively. The proposed model along with the identification of the heart attack, it also analyzes the heart beat rate time to time based on the sensor. This analysis helps the doctor to recognize the part that is blocked and that makes the patient to suffer from cardiac arrest or survival problems in future. Traditional approaches use either principal component analysis or neural networks for identifying those parts but the proposed model extends the de-noising auto encoder that has the capability to prevent similar identity learning features. The model has achieved +3.1% accuracy improvements when compared to the traditional CNN.    

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References

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Classification of Encoder and Decoders

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Published

22.02.2023

How to Cite

M., J. ., Narayana, J. L. ., Pathanjali Sastri, A. ., & Sri. T., S. . (2023). Hybrid Auto Encoder for Dimensionality Reduction in Ct Scan Images of Heart. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 349–357. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2638

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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