RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan, Meenal; Ramasamy, Asokan

doi:10.1177/1063293x211010542

Cited by 34 publications

(13 citation statements)

References 16 publications

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“…A recent work was proposed by [ 12 ] that was based on AlexNet and GoogleNet, which were trained on a huge amount of real dataset, and claimed 89% of classification. On the other hand, a state-of-the-art work was proposed by [ 13 ], where they extracted the shape-based and texture-based features by utilizing the wavelet transform and histogram of oriented gradient, respectively. For classification purpose, they employed one of the well-known machine learning algorithms such as random forest.…”

Section: Introductionmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

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Section: Introductionmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…In (Vijayakumar et al, 2021), and the author contributes a precision classification technique to classify the breast cancer images using Deep Neural Network. Classification of brain tumour (Thayumanavan and Ramasamy, 2021) was carried out by Support Vector Machine (SVM), Random Forest Classifier (RFC) and Decision Tree (DT) and performance analysis has been done by the parameters such as specificity, sensitivity accuracy. It is observed from these literature studies that the authors focus only on the few metrics such as accuracy, sensitivity and specificity.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

Sabarivani

Ramadevi

2022

Concurrent Engineering

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In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.

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“…Thayumanavan et al in [1] proposed using a median filter to optimize the shedding of the brain in MRI. The filter extracted abnormal tissues in low contrast to find the edges of infected tissues.…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of tumors for their location, size, and shape is complex [1,2]. MRI scans can offer indepth information about the soft tissues in the human body and provide an outline of a tumor if it exists [2,[3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Effective Diagnosis System for Brain Tumor Detection and Classification

Alsheikhy¹,

Azzahrani²,

Alzahrani³

et al. 2023

Computer Systems Science and Engineering

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A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

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RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Cited by 34 publications

References 16 publications

A Precise Medical Imaging Approach for Brain MRI Image Classification

A Precise Medical Imaging Approach for Brain MRI Image Classification

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

An Effective Diagnosis System for Brain Tumor Detection and Classification

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