Classification of Brain Lesion using K- Nearest Neighbor technique and Texture Analysis

Ali, Alyaa H.; Al-hadi, Sara Abd; Naeemah, Maysaa R.; Mazher, Alaa Noori

doi:10.1088/1742-6596/1178/1/012018

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…ML is widely used for tumor classification into the appropriate class 9 . In this study, four models are utilized in the classification process: KNN (fine KNN), 18,19 SVM (cubic SVM), 20 naïve Bayes (kernel), and DT (fine tree) 21 . These classifiers are specifically used because of their superior in the multiclassification task of brain tumor MRI images.…”

Section: Methodsmentioning

confidence: 99%

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

Shaker

El-Hossiny

Kandil

et al. 2023

Int J Imaging Syst Tech

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Brain tumors have recently become a widespread phenomenon that affects almost all age ranges. Magnetic resonance imaging (MRI) is a widely used technique nowadays to diagnose this life‐threatening disease. Therefore, using a computer‐aided diagnosis system is essential for the early identification and classification of brain tumors. This work aims to develop a fully automated classification framework for MRI brain tumor images using machine learning (ML) algorithms. In this article, four ML algorithms such as support vector machines (Cubic SVM), k‐nearest neighbors (fine KNN), decision tree (fine tree), and naive Bayes (Kernel) were implemented with two feature extraction approaches: histogram of oriented gradients (HOG) and a bag of features (BOF) approach. The novelty of this work is based on the original usage of BOF and HOG approaches over different types of ML algorithms for MRI brain tumor image classification. In addition, combining two public datasets that contain three brain tumor types to generalize and improve the proposed work. The results showed that the fine KNN algorithm achieved the greatest success among all algorithms using the HOG method with an accuracy of 98.62% on 6328 MRI images.

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Section: Methodsmentioning

confidence: 99%

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

Shaker

El-Hossiny

Kandil

et al. 2023

Int J Imaging Syst Tech

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show abstract

“…Machine learning is widely used for tumor classification into appropriate classes, e.g., tumor substructure (complete/non-enhanced/enhanced) [193], tumor and nontumor [26], and benign and malignant tumor [15,47,163,194,195]. KNN [196], SVM, nearest subspace classifier, and representation classifier [143] are supervised, whereas FCM [197,198], hidden Markova random field [199] selforganization map [101], and SSAE [200] are unsupervised methods.…”

Section: Classification Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

173

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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“…Challenges with clustering data of varying sizes and densities [44] Discriminant Method has an efficient method for feature extraction and dimension reduction [45] Assigns exact values to outcomes of various actions [45] It is restricted to one output attribute [45] SVM Analysis Model SVM Method is a best classifier for categorising two or more categories [46] Provides better accuracy compare to other classifier easily handle complex nonlinear data points and easily handle complex nonlinear data points [47] It is expensive [47] k-NN Analysis Model k-NN Method is a nonparametric learning set of a classification algorithm that categorises objects based on the closest pixel [48] It is easy to implement [48] Sensitive to noise and requires large storage space [48] Decision Tree Analysis Model Decision tree Method minimizes the ambiguity of complicated decisions [49] It handles both numerical and categorical data [49] It is an unstable classifier, for example performance of classifier is depend upon the type of dataset [49] The clustering technique is commonly used during the segmentation process. K-means is computationally faster than fuzzy c-means [50].…”

Section: Decision Tree Analysis Modelmentioning

confidence: 99%

Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review

Kandaya,

Abdullah,

Saad

et al. 2024

IJ-AI

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<p>Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.</p>

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Classification of Brain Lesion using K- Nearest Neighbor technique and Texture Analysis

Cited by 7 publications

References 5 publications

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

Brain tumor detection and classification using machine learning: a comprehensive survey

Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review

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