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Indian Journal of Public Health Research & Development
Year : 2019, Volume : 10, Issue : 7
First page : ( 438) Last page : ( 443)
Print ISSN : 0976-0245. Online ISSN : 0976-5506.
Article DOI : 10.5958/0976-5506.2019.01609.7

An Automatic Disease Early Prediction and Diagnosis Recommendation Framework for Brain Tumours

Bodapalli Nandan1, Rao Kunjam Nageswara2, Dora Parada Vara Prasad3

1Research Scholar, Department of CS & SE, AUCE(A) at Andhra University, Visakhapatnam, AP

2Professor, Department of CS & SE, AUCE(A) at Andhra University, Visakhapatnam, AP

3MBBS, MD Cardioanesthesia at Omni RK Hospital, Seven Hill Road, Visakhapatnam, Andhra Pradesh, India

Online published on 19 August, 2019.

Abstract

Use of computation techniques in medical research have progressed to a greater extend with the availability of higher order complex and accurate algorithms for detecting diseases. The medical diagnosis processes with human intervention cannot match with the demands from the consumers as the human process is slow and less accuracy as it completely depends on the skills of the individuals. One of the prominent medical diagnosis is detection of brain tumours cells. Human brain being the most important component of the body, can cause multiple other life-threatening diseases. These diseases are caused due to the presence of tumour in the brain cell. Hence, this research attempts to propose a higher accurate tumour detection algorithm. The improved accuracy is achieved due to the novel proposed dynamic intensity-based MR image enhancement algorithm and the proposed adaptive coefficient-based segmentation algorithm. Further, it is also been observed that, the presence of the tumour in the brain cortexes can lead to other diseases as well. These diseases from a large number of possibility space, can take a huge amount of time to diagnose and further propose medications. Hence a reduction of the possibility space and the reduced diagnosis process can give more time for medication to save the precious human life. Thus, yet another objective of this research is to automate the disease early prediction by recommending the diagnosis for reduced number of diseases by applying tumour centroid detection and mapping to human brain cortex functionalities. The work demonstrates a very high 97% accuracy for the detection and early prediction.

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Keywords

Tumour Detection, MR Image Enhancement, Adaptive segmentation, Tumour Region Detection, Disease Early Prediction, Diagnosis Recommendation.

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