Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques
Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-5 |
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| Year of Publication : 2023 | ||
| Author : Jaya H. Dewan, Sudeep D. Thepade, Pradnya Deshmukh, Sharvari Deshmukh, Pooja Katpale, Kartik Gandole |
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| DOI : 10.14445/22315381/IJETT-V71I5P232 | ||
How to Cite?
Jaya H. Dewan, Sudeep D. Thepade, Pradnya Deshmukh, Sharvari Deshmukh, Pooja Katpale, Kartik Gandole, "Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 303-312, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P232
Abstract
Using Magnetic Resonance Imaging (MRI) to find brain tumors is difficult for contemporary medical imaging research. Basically, a brain tumor is an expansion of aberrant brain cells that expand erratically and seemingly uncontrolled. Meningioma, Glioma, and Pituitary arethe three kinds of tumors that are most frequently seen. Early identification is essential for the successful treatment of brain tumors. With the development of medical imaging, doctors now employ various imaging methods, such as fMRI, EEG, etc., to diagnose brain tumors. These imaging methods can help clinicians establish a precise diagnosis and create a treatment strategy by providing details on brain tumours' location, size, and shape. Feature extraction and classification are two steps in the categorization of brain tumors. Two traditional manual feature extraction methods were frequently utilized in certain earlier research to extract details like the intensity and texture of images of brain tumors. This work employs the "GLCM (Grey Level Co-occurrence Matrix)" approach for feature extraction. The generated feature set is provided to machine learning (ML) algorithms, including "K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), Naive Bayes (NB), and Random Forest(RF)". According to experimental results, random forest yields the highest accuracy of 91.04%. The proposed methodology helps classify the different brain tumor classes like glioma, pituitary, meningioma, or no tumor.
Keywords
Brain tumor, Decision tree, GLCM, K-nearest neighbors, Logistic regression, MRI, Naïve bayes, Random forest, Support vector machine.
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