Brain Tumor Detection and Classification of MRI Brain Images Using Morphological Operations

Gezimati, Mavis; Rushambwa, Munyaradzi Charles; Jeeva, J. B.

doi:10.1007/978-981-13-1477-3_11

Cited by 3 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23] authors have extracted area, perimeter, and eccentricity features for the classification of brain tumor using k-medoid clustering method and morphological operations. In [24] authors have proposed a hybrid ensemble approach based on the majority voting method, which incorporates RF, KNN and DT for classification of brain tumors by extracting Stationary Wavelet Transform (SWT), Gray Level Cooccurrence Matrix (GLCM) and Principal Component Analysis (PCA) features.…”

Section: Literature Surveymentioning

confidence: 99%

Local Frequency Descriptor and Hybrid Features for Classification of Brain Magnetic Resonance Images using Ensemble Classifier

Shruthi¹,

M²

2021

IJACSA

View full text Add to dashboard Cite

A brain tumor is an irregular development of cells in the human brain that causes problems with the brain's normal functionalities. Early detection of brain tumor is an essential process to help the patient to live longer than treatment. Hence in this paper, a hybrid ensemble model has been proposed to classify the input brain MRI images into two classes: brain MRI images having tumor and brain MRI images with no tumor. The hybrid features are extracted by analyzing the texture and statistical properties of brain MRI images. Further, the Local Frequency Descriptor (LFD) technique is employed to extract the prominent features from the brain tumor region. Finally, an ensemble classifier has been developed with the combination of Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbour (KNN) technique to successfully classify the brain MRI images into brain tumor MRI images and non-tumor brain MRI images. The proposed model is tested on the Kaggle brain tumor dataset and the performance of the method is evaluated in terms of accuracy, sensitivity, specificity, precision, recall and f-measure (f1 score-harmonic mean of precision and recall). The results show that the proposed model is promising and encouraging.

show abstract

Section: Literature Surveymentioning

confidence: 99%