Open Access

ARTICLE

Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier

Simy Mary Kurian¹, Sujitha Juliet Devaraj^1,*, Vinodh P. Vijayan²

1 Karunya Institute of Technology and Science, Coimbatore, India
2 Albertian Institute of Science and Technology, Kochi, India

* Corresponding Author: Sujitha Juliet Devaraj. Email:

(This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(2), 2093-2109. https://doi.org/10.32604/cmc.2021.018090

Received 24 February 2021; Accepted 21 April 2021; Issue published 21 July 2021

Abstract

Magnetic resonance imaging (MRI) is an essential tool for detecting brain tumours. However, identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental obstructions. In order to overcome these problems, a Gamma MAP denoised Strömberg wavelet segmentation based on a maximum entropy classifier (GMDSWS-MEC) model is developed for efficient tumour detection with high accuracy and low time consumption. The GMDSWS-MEC model performs three steps, namely pre-processing, segmentation, and classification. Within the GMDSWS-MEC model, the Gamma MAP filter performs the pre-processing task and achieves a significant increase in the peak signal-to-noise ratio by removing noisy artefacts from the input brain image. After pre-processing, Strömberg wavelet transform segmentation is carried out to partition the pre-processed image into a number of blocks based on the features extracted from the image. Finally, the maximum entropy classifier identifies and locates the tumour from the input image based on extracted features with high accuracy and minimal error rate. Using a number of MRI images, experimental evaluation and comparison of the proposed model and existing methods is carried out on the basis of four metrics: peak signal-to-noise ratio, tumour detection accuracy, error rate, and tumour detection time with respect to MRI image size. The proposed model offers superior performance in terms of all four metrics.

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