Brain tumors are among the most lethal and complex neurological
disorders, often requiring precise diagnosis for effective treatment.
Magnetic Resonance Imaging (MRI) plays a crucial role in detecting
and classifying brain tumors due to its high-resolution imaging and
contrast capabilities. However, accurate classification remains
challenging due to overlapping tissue intensities, noise, and irrelevant
features. Existing classification techniques often suffer from low
precision due to redundant or non-discriminative features, especially
when working with unsupervised clustering methods like K-Means.
These shortcomings can result in misclassification, delayed treatment,
and poor prognosis. This work proposes an improved method for MRI
brain tumor classification by integrating K-Means clustering with an
enhanced feature selection mechanism. Initially, preprocessing
techniques such as grayscale conversion and histogram equalization
are applied. K-Means clustering is used to segment the tumor region,
followed by extraction of statistical and texture-based features (e.g.,
mean, entropy, contrast, GLCM). An enhanced feature selection
approach based on Mutual Information and Principal Component
Analysis (PCA) is used to reduce dimensionality and retain only the
most relevant features. A classifier such as Support Vector Machine
(SVM) is finally used for tumor type prediction. The proposed method
was evaluated on a standard MRI brain tumor dataset. Experimental
results showed improved classification accuracy (95.3%), precision
(94.6%), sensitivity (95.1%), and F1-score (94.8%) compared to
existing techniques such as basic K-Means and PCA-SVM.
K. Nagalakshmi, J. Rajalakshmi
Sethu Institute of Technology, India
MRI, Brain Tumor, K-Means Clustering, Feature Selection, Classification
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 16 , Issue: 1 , Pages: 3778 - 3781 )
Date of Publication :
April 2025
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