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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44705

BRAIN TUMOUR CLASSIFICATION USING MACHINE LEARNING THRESHOLDING

PERIYAKARUPPAN K, KARTHIK S, KARTHIKEYAN N

Abstract

In recent era, it is of the utmost importance to perform early identification and diagnosis by means of MRI due to the high mortality rate associated with brain tumours. Because of the intricate nature of the brain structure and the interconnection of its many different parts, it is famously difficult to treat a brain tumour in an effective manner. The accurate and efficient division of a brain tumour is still a difficult challenge to tackle, despite the numerous various technologies that are currently available for doing so. Despite these advancements, the problem remains difficult. Dealing with the many different kinds of tumours presents its own specific challenges when seeking to divide and classify them. Due to the complexity of the situation, using only a single imaging modality may make it challenging to undertake full segmentation and classification of a brain tumour. In this paper, we model a threshold-based classification based on the selection of certain features set. The classification algorithm used in the study is random forest. The model is trained and tested in python tool. The results of simulation show an increased accuracy than other methods. sets of EEG and the results show that the proposed method has higher range of classification accuracy than the other methods.

Keywords

brain tumour, thresholding, classification.

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