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Predictive Analytics of Brain Tumour Analysis via Recurrent Neural Networks.
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| Author:
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V. KAKULAPATI, PAMIREDDY SINDU, RACHAMALLA SRIYA
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| Abstract:
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The main organ and the central nerve system of human is brain. Impairment in the brain is the growth of abundance and departed cells close to the bend of the brain is called as cerebellum tumour. The blood supply of a human or individual brain is disturbed or condensed leads to brain tumour. When tumour occurs in the brain which denies the brain of oxygen and nutrients due to the cells in the brain are dead. Researchers are developing AI techniques which will play a key role in formative healing techniques and expecting the diagnosis of tumour long-sufferings. To overcome this, health care providers are collecting dissimilar types of data such as biomedical, behavioural and activity, and analysing these data by utilizing machine learning techniques for extracting hidden patterns for brain tumour prediction. In this work, we implemented machine learning technique such as RNN (Recurrent Neural Networks) for achieving excellent results; discover hidden patterns of the data appropriate to the asymmetrical surveillance time occurrences. Approximations of real time data reveal that our proposed techniques are more accurate predictable results. This method will give prior knowledge to diseased persons about brain tumours.
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| Keyword:
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brain, machine, algorithm, recurrent, prediction, behaviour, patterns
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.02.0092
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| Download:
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