Improving Prediction Model Accuracy by Wavelet Transform Based on MRI Images of Brain Tumour Patients

Authors

  • Saniya Suhail EWIT, India
  • Savita S Dodakenchannavar EWIT, India
  • Shilpashree GL EWIT, India
  • Swetha M EWIT, India
  • Hemanth YK EWIT, India

Keywords:

Machine learning,, Denoising wavelet transform, MRI images, Histogram, Glioma brain tumour, Linear Regression

Abstract

Medical imaging provides proper diagnosis of brain tumour. Various techniques are implemented to detect the brain tumour from MRI images. One among them is the Denoising wavelet transform (DWT) method which is used to improve the accuracy of a prediction model by making use of MRI images in order to predict the overall survival time of brain tumour patients. Wavelet transform method detects the location and size of the tumour. The proposed methodology consists of image acquisition, Calculation of tissue density maps, statistical analysis. MRI provides generous information about the human soft tissue, which helps in the recognition of brain tumour. Image Segmentation categorises pixels into sections and hence defines the object regions. This paper proposes the image and feature fusion techniques to improve the accuracy of the prediction model.

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Published

2025-11-26

How to Cite

[1]
S. Suhail, S. S. Dodakenchannavar, S. GL, S. M, and H. Y., “Improving Prediction Model Accuracy by Wavelet Transform Based on MRI Images of Brain Tumour Patients”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 203–207, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

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