Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing &amp; KNN

Nida-Ur-Rehman, Qazi; Ahmed, Imran; Masood, Ghulam; Najam-U-Saquib,; Khan, Muhammad Aurangzeb; Adnan, Awais

doi:10.14569/ijacsa.2017.080434

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…In Nida-Ur-Rehman et al ( 2017 ), brain tumors are broken down into four distinct categories. The dataset utilized in this study consists of two thousand magnetic resonance imaging (MRI) scans with a clinical and expert opinion from the FCPS neurosurgeon.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model was trained on 75% of the images, and 25% were used for validation (Shoaib et al, 2022 ). However, gaps (Nida-Ur-Rehman et al, 2017 ; Irmak, 2021 ; Maqsood et al, 2022 ; Shoaib et al, 2022 ) still need to be filled while addressing brain tumor segmentation. Firstly, there are more than 100 types of brain tumors in the world that need to be segmented.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

Kiran,

Zeb,

Rehman

et al. 2024

Front. Comput. Neurosci.

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Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

Kiran,

Zeb,

Rehman

et al. 2024

Front. Comput. Neurosci.

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show abstract

“…Q Nida-Ur-Rehman et al [4] aimed to segment and classify the four most prevalent type of brain tumors. In the proposed model, morphological operations are carried out after pre-processing, and segmentation is done using histogram differencing with rank filter.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

Dewan¹,

Thepade²,

Deshmukh³

et al. 2023

IJETT

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The detection of brain tumors using Magnetic Resonance Images(MRI) 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 are the 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 a variety of 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 the location, size, and shape of brain tumors. 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 Neighbours(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 a maximum accuracy of 91.04%. The proposed methodology helps to classify the different brain tumor classes like glioma, meningioma, pituitary, or else no tumor.

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“…A brain tumor is one of the major health problems related to human brain abnormalities [1]- [3]. It is a collection or a combination of unnatural tissue in the brain [4].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Lamrani¹,

Cherradi²,

Gannour³

et al. 2022

IJACSA

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A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors.

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Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Cited by 6 publications

References 18 publications

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

Brain Tumor Detection using MRI Images and Convolutional Neural Network

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