Edge Detection with Neuro-Fuzzy Approach in Digital Synthesis Images

Zribi, Fatma; Ellouze, Noureddine

doi:10.14569/ijacsa.2016.070448

Cited by 1 publication

(1 citation statement)

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“…In [4] authors have implemented a novel approach for edge detection in two steps F-test for identifying the pixel variations and T-test based on contrast function which observes the edges in all four direction. The proposed model in [5] presents an edge detection model based on Neuro fuzzyapproach. The authors have developed an edge detection model in [6] by incorporating active contour model driven from cellular neural network.…”

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

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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.

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Section: Literature Surveymentioning

confidence: 99%