Clinical Application of Neural Network for Cancer Detection Application

Authors

  • R Kishore Kanna Jerusalem College of Engineering
  • R Ravindraiah Madanapalle Institute of Technology & Science
  • C Priya Siddarth Institute of Engineering and Technology
  • R Gomalavalli Siddarth Institute of Engineering and Technology
  • Nimmagadda Muralikrishna PACE Institute of Technology and Sciences

DOI:

https://doi.org/10.4108/eetpht.10.5454

Keywords:

Cancer, Neural Network, Cells, ML

Abstract

 

INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners.

OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer.

METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer.

RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others.

CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.

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References

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Published

18-03-2024

How to Cite

1.
Kishore Kanna R, Ravindraiah R, Priya C, Gomalavalli R, Muralikrishna N. Clinical Application of Neural Network for Cancer Detection Application. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 18 [cited 2024 Oct. 14];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5454