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

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.