Identification and Detection of Tumour Hypoxia from Multimodal Microscopy using Deep Neural Network

9 Pages Posted: 16 Mar 2020 Last revised: 16 Apr 2020

See all articles by Dr.Usha Mahalingam

Dr.Usha Mahalingam

Velammal Engineering College,India

Dr.P. Prittopaul

Velammal Engineering College,India

Date Written: February 21, 2020

Abstract

Hypoxia is a situation which indicates low level of oxygen content in the cells. The majority of the tumour treatments contain chronic and acute radiotherapy and chemotherapy. It is important to estimate the proportion of hypoxic regions in tumours to improve clinical prognosis of treatment .In this paper we are going to develop another machine learning based philosophy to computerize this estimation, where the primary test is the way that the clinical explanations accessible for preparing the techniques comprise of the aggregate number of normoxic, incessantly hypoxic and intensely hypoxic areas with no sign of their area in the picture. The proposed methodologies indicate high connection esteems as for the clinical explanations. The result will be to detect whether a cell is affected with hypoxic condition.

Keywords: Hypoxia, chronic, normoxic, machine learning

JEL Classification: O30

Suggested Citation

Mahalingam, Usha and Prittopaul, P., Identification and Detection of Tumour Hypoxia from Multimodal Microscopy using Deep Neural Network (February 21, 2020). Proceedings of the 4th International Conference: Innovative Advancement in Engineering & Technology (IAET) 2020, Available at SSRN: https://ssrn.com/abstract=3554914 or http://dx.doi.org/10.2139/ssrn.3554914

Usha Mahalingam (Contact Author)

Velammal Engineering College,India ( email )

Velammal Nagar
Red Hills
Chennai, Tamilnadu 600066
India

P. Prittopaul

Velammal Engineering College,India ( email )

Velammal Nagar
Red Hills
Chennai, Tamilnadu 600066
India

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