RETRACTED ARTICLE: Brain image classification by the combination of different wavelet transforms and support vector machine classification

Mishra, Shailendra; Deepthi, V. Hima

doi:10.1007/s12652-020-02299-y

Cited by 31 publications

(24 citation statements)

References 33 publications

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“…Further, the classification mean CPU time in seconds also depicted in Table 9 shows the performance analysis of various algorithms. Even if we go through various articles on brain tumor classification and detection, we closely follow some very recent works by Mishra et al (2020) [14] and Abbasi et al (2017) [23]. While most of the works follow certain types of methods for feature extraction, then a sole classifier for the classification task.…”

Section: Resultsmentioning

confidence: 96%

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An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

Jena

Nayak

Saxena

2021

Machine Vision and Applications

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Brain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our study is divided into two stages: supervised machine learning-based tumor classification and image processing-based region of tumor extraction. For this job, seven methods have been used for texture feature generation. We have experimented with various state-of-the-art supervised machine learning classification algorithms such as support vector machines (SVMs), K-nearest neighbors (KNNs), binary decision trees (BDTs), random forest (RF), and ensemble methods. Then considering texture features into account, we have tried for fuzzy C-means (FCM), K-means, and hybrid image segmentation algorithms for our study. The experimental results achieved a classification accuracy of 94.25%, 87.88%, 89.57%, 96.99%, and 97% with SVM, KNN, BDT, RF, and Ensemble methods, respectively, on FLAIR-, T1C-, and T2-weighted MRI, and the hybrid segmentation attaining 90.16% mean dice score for tumor area segmentation against ground-truth images.

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Section: Resultsmentioning

confidence: 96%

“…In [14], Mishra et al (2020) present brain tumor MRI classification using a support vector machine (SVM) by con-sidering wavelet feature extraction such as DWT, SWT, and DMWT. In [15], Gumei et al (2019) proposed a regularized extreme learning machine (RELM) brain tumor classification method based on a machine learning approach.…”

Section: Related Workmentioning

confidence: 99%

An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

Jena

Nayak

Saxena

2021

Machine Vision and Applications

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“…If there were N samples, each sample was, respectively, regarded as the test set, and the remaining N-1 samples were regarded as the training set. Then, in the training set, K-fold Cross Validation was used for parameter optimization (c, γ) and the parameter group (c, γ) with the highest classification accuracy in the training set was selected to construct the classification model ( Mishra and Deepthi, 2020 ). Here, we set the range of ( c , γ) to (2 –7 , 2 7 ).…”

Section: Methodsmentioning

confidence: 99%

Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data

et al. 2022

Front. Neurosci.

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Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.

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“…Mishra et al [ 31 ] proposed an efficient method for magnetic resonance imaging (MRI) brain image classification based on different wavelet transforms such as discrete wavelet transform (DWT) and stationary wavelet transforms (SWT). Dual-tree M-band wavelet transform (DMWT) was used for feature extraction and selection of coefficients for classification using support vector machine classifiers.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

Kader

Zhang

et al. 2021

Diagnostics

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The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

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RETRACTED ARTICLE: Brain image classification by the combination of different wavelet transforms and support vector machine classification

Cited by 31 publications

References 33 publications

An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature

Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

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