Classification of magnetic resonance images for brain tumour detection

Kurmi, Yashwant; Chaurasia, Vijayshri

doi:10.1049/iet-ipr.2019.1631

Cited by 20 publications

(15 citation statements)

References 45 publications

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“…

\begin{equation}PSNR = 10 \times {\log _{10}}\left( {\frac{{MA{X^2}}}{{MSE}}} \right)\end{equation}

where MAX is the maximum pixel value in an m × n image, and MSE is the MSE. PSNR is the most common way to measure image quality, the higher values of the PSNR mean the better reconstruction of the image [41]. The healthy MRIs of Class‐II (a6, a7) resulted in a black image (f6, f7) without any white area.…”

Section: Resultsmentioning

confidence: 99%

“…To increase the accuracy, sensitivity, specificity, and precision, combinational methods were considered like fractional Jaya whale optimizer deep convolutional neural network with the DeepJoint segmentation method [39], and Grid‐based image segmentation‐weighted bee swarm optimization‐K‐means clustering [40]. Some researchers used a set of handcrafted features using a segmentation‐based localized region to train and test the SVM and multilayer perceptron classifiers, but the accuracy parameters showed some errors in the classification [41]. Superpixels template‐based K‐means [42], and a genetic algorithm‐based feature selection detected the brain tumours with a specificity of about 100% [43].…”

Section: Related Workmentioning

confidence: 99%

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Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

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“…

\begin{equation}PSNR = 10 \times {\log _{10}}\left( {\frac{{MA{X^2}}}{{MSE}}} \right)\end{equation}

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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“…Magnetic Resonance Imaging (MRI) is a medical imaging modality used to visualize the internal organs of the human body. The MRI is widely used for the diagnosis of a broad spectrum of diseases like ischemic stroke [1], Autism Spectrum Disorder (ASD) [2], Parkinson's disease [3], brain tumors [4], Schizophrenia [5], intracranial Tuberculosis [6], pancreatic cancer [7], Osteo Arthritis [8], prostate cancer [9] and Endometriosis [10]. Because of hardware limitations, images obtained from low-field MRI scanners are of low resolution, low acutance, and low contrast.…”

Section: Background and Problem Domainmentioning

confidence: 99%

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Shiney¹,

Saleem²,

Rajinikanth³

et al. 2022

Computers, Materials &Amp; Continua

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Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.

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“…The chosen feature is applied to the input image, and if it matches, the image is classified correctly. [47][48][49][50][51]. CNN is a technique that takes an image as input and adds values in the form of weight and bias to the various parts of the features in an A kernel or filter is used to detect features [55].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review on Brain Tumor Detection Using Convolutional Neural Network

Kumari

Bhat

2022

IJCSBE

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Background/Purpose: The automatic identification of brain tumor types is important for advancing remedy and boosting survival of patients. In nowadays, magnetic resonance imaging is only used to effectively explore a variety of brain cancer. Since manual categorization of brain cancer requires experts and is only suitable restricted collection of clear MRI pictures, study of Convolutional Neural Network model for automatic diagnosis of brain tumor and how neural network technics are applied in images to detect tumor is proposed in this review paper. Design/Methodology/Approach: Various Scholarly articles and websites are referred and studied to gather information for this review paper. Findings/Result: Convolutional neural network and its different layers in image processing. Originality/Value: This review-based research article is a brain tumor study detection implementing a Cnn Architecture as well as the research gaps and research Agenda. Paper type: Literature Review

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Classification of magnetic resonance images for brain tumour detection

Cited by 20 publications

References 45 publications

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

A Post-Processing Algorithm for Boosting Contrast of MRI Images

A Review on Brain Tumor Detection Using Convolutional Neural Network

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