Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science

Wahid, Fazli; Ghazali, Rozaida; Fayaz, Muhammad; Shah, A.

doi:10.14257/ijbsbt.2016.8.6.10

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…For this, we have applied the convolutional neural network for brain MRI classification. There are many classifiers, such as support vector machine, k-nearest neighbor, artificial neural network, random forest, and we have applied these classifiers in our previous studies [24,42,67]. However, a convolutional neural network performs the best in the sense of accuracy in this study.…”

Section: Resultsmentioning

confidence: 87%

“…The overall accuracy of the results of the proposed method is determined by using the performance evaluation factors, such as Kappa statistics, which is a measurement metric that carried out the comparison of the observed accuracy with the expected accuracy. This is considered true positive (TP), which indicates the accurate prediction of the positive class, false positive (FP), which indicates the inaccurate prediction of the positive class [24,42]. The precision is a performance matric that uses TP and FP factors to define the degree of measurements given in Equation (6).…”

Section: Resultsmentioning

confidence: 99%

“…High accuracies in brain MRI classification have been achieved by Wahid et al [42], who proposed a method based on statistical moments and probabilistic techniques. Statistical moments have been employed for feature extraction, and ANN has been used for feature reduction.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, it is of utmost importance to apply an appropriate filter for noise removal for images. In the proposed work, we have used the MR grayscale brain images, and these types of images are affected by salt-and-pepper noise [42,61]. For this purpose, we have used a median filter to remove salt-and-pepper noise from MR brain images.…”

Section: Preprocessingmentioning

confidence: 99%

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An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Fayaz

Torokeldiev²,

Turdumamatov

et al. 2021

Sensors

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In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications.

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Fayaz

Torokeldiev²,

Turdumamatov

et al. 2021

Sensors

Self Cite

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“…Fayaz et al (2016) suggested a three stages approach for brain MRI classification and hence achieved high accuracy. Wahid et al (2016) suggested a method for identifying normal and abnormal MR images using three stages such as preprocessing, feature extraction and then apply probabilistic classifier and obtained high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced feature extraction technique for brain MRI classification based on Haar wavelet and statistical moments

Ullah¹,

Lee²,

Fayaz³

2019

Int. j. adv. appl. sci.

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Many methods have been proposed to classify the MR brain images automatically. We have proposed a method based on a Neural Network (NN) to classify the normality and abnormality of a given MR brain image. This method first employs a median filter to minimize the noise from the image and converted the image to RGB. Then applies the technique of Discrete Wavelet Transform (DWT) to extract the important features from the image and color moments have been employed in the feature reduction stage to reduce the dimension of the features. The reduced features are sent to Feed-Forward Artificial neural network (FF-ANN) to discriminate the normal and abnormal MR brain images. We applied this proposed method on 70 images (45 normal, 25 abnormal). The accuracy of the proposed method of both training and testing images are 95.48%, while the computation time for feature extraction, feature reduction, and neural network classifier is 4.3216s, 4.5056s, and 1.4797s, respectively.

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An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion‐based machine learning techniques

Mehrotra,

Ansari,

Agrawal

et al. 2023

Int J Imaging Syst Tech

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Today, the histological study of biopsy specimens is still used to diagnose brain tumors (BTs). This existing procedure is intrusive, arduous, and liable to mistakes. These downsides highlight the standing of employing a completely computerized process for identifying the evolution of tumors in the brain. A primary BT affects an estimated 0.7 million persons in the United States now and more are expected to be detected in the coming years. The ability to categorize magnetic resonance (MR) brain images into ordinary and pathological categories has the boundless ability to significantly diminish the burden on the radiologist. Pre‐processing, extraction, and reduction of features along with their classification are the parameters of statistical‐based methodologies that have been frequently used for this purpose. In this work, an enhanced framework for the identification of the BT is proposed using discrete wavelet transform (DWT), deep convolutional network (DCN), and machine learning (ML). As DWT is primarily used for image compression and denoising applications however in the presented research work it has been utilized for extricating pivotal features from the MR images using the feature fusion technique. DCN is also utilized for the extraction of pivotal deep features which are then combined with the wavelet‐based features for the purpose of BT identification. The classification of tumorous and non‐tumorous MR images is done using ML applications. The results obtained from the proposed model exhibit an utmost accuracy of 99.5% with an area under curve of 1 in identifying tumorous and non‐tumorous MR images as compared to various state‐of‐the‐art models. The proposed model can be efficiently used for assisting radiologists and medical experts in validating their decisions for BT identification.

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Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science

Cited by 13 publications

References 13 publications

An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Enhanced feature extraction technique for brain MRI classification based on Haar wavelet and statistical moments

An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion‐based machine learning techniques

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