A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition

Al-Saffar, Zahraa A.; Yıldırım, Tülay

doi:10.1109/access.2020.2980728

Cited by 37 publications

(12 citation statements)

References 43 publications

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“…A support vector machine (SVM) based on a radial basis kernel function is selected as the pixel classifier. In the training of the classifier, a layer of the patient with a tumor image is randomly selected, and 60 points inside and outside the tumor are taken as training samples [17].…”

Section: Mri Brain Tumor Subspace Clustering Algorithm Based On Multimentioning

confidence: 99%

“…Figure 4 shows the average clustering results of the same patient training layer with different neighborhood sizes. It can be seen from the figure that the optimal neighborhood size appears between 14 and 26, and considering the clustering time and the clustering accuracy of small tumors, the neighborhood value should not be too large, so the neighborhood optimization range is 10-30 [17]. Figure 5 shows the clustering results of each training layer of 10 patients.…”

Section: Parameter Range Determinationmentioning

confidence: 99%

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Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

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In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.

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Section: Mri Brain Tumor Subspace Clustering Algorithm Based On Multimentioning

confidence: 99%

Section: Parameter Range Determinationmentioning

confidence: 99%

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

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“…The 438 excluded articles consisted of 172 conference abstracts, 140 articles not utilizing machine learning, 62 not representing original research, 22 not published in the English language, 15 not investigating gliomas, 11 not utilizing MRI, magnetic resonance spectroscopy (MRS), or positron emission tomography (PET) imaging, 9 not utilizing human subjects, and 7 duplicate articles. The remaining 697 articles underwent further review, of which 685 articles were excluded and 12 articles ( 7 – 18 ) investigating the use of machine learning to identify gliomas in datasets which include non-glioma images were identified for inclusion in the final analysis.…”

Section: Resultsmentioning

confidence: 99%

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review

et al. 2021

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PurposeMachine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.Materials and MethodsFour databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy included both keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, as well as related terms. The review was conducted in stepwise fashion with abstract screening, full text screening, and data extraction. Quality of reporting was assessed using TRIPOD criteria.ResultsA total of 11,727 candidate articles were identified, of which 12 articles were included in the final analysis. Studies investigated the differentiation of normal from abnormal images in datasets which include gliomas (7 articles) and the differentiation of glioma images from non-glioma or normal images (5 articles). Single institution datasets were most common (5 articles) followed by BRATS (3 articles). The median sample size was 280 patients. Algorithm testing strategies consisted of five-fold cross validation (5 articles), and the use of exclusive sets of images within the same dataset for training and for testing (7 articles). Neural networks were the most common type of algorithm (10 articles). The accuracy of algorithms ranged from 0.75 to 1.00 (median 0.96, 10 articles). Quality of reporting assessment utilizing TRIPOD criteria yielded a mean individual TRIPOD ratio of 0.50 (standard deviation 0.14, range 0.37 to 0.85).ConclusionSystematic review investigating the identification of gliomas in datasets which include non-glioma images demonstrated multiple limitations hindering the application of these algorithms to clinical practice. These included limited datasets, a lack of generalizable algorithm training and testing strategies, and poor quality of reporting. The development of more robust and heterogeneous datasets is needed for algorithm development. Future studies would benefit from using external datasets for algorithm testing as well as placing increased attention on quality of reporting standards.Systematic Review Registrationwww.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020209938, International Prospective Register of Systematic Reviews (PROSPERO 2020 CRD42020209938).

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“…However, the designed method did not minimize the brain tumor classification time. Al-Saffar ZA (2020) developed a mutual information-accelerated singular value decomposition (MI-ASVD) in Al-Saffar and Yildirim (2020) to identify the MRI brain images into various classes. However, the designed approach failed to improve the accuracy of segmentation.…”

Section: Introductionmentioning

confidence: 99%

An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

Kurian

Juliet

2022

Soft Comput

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Brain tumors are the second important origin of death worldwide. The early and exact identification of brain tumors is significant for the healing process. With accelerating diagnoses, medicine as well as pricing, quantum computing permits disruptive cases to providers. Quantum improved deep learning was especially significant to the sector. However, the conventional machine learning method faces main challenges to achieve accurate brain tumor detection with MRI images. Therefore, this paper proposes a novel technique called Lee sigma filtered histogram segmentation (LSFHS) for accurately detecting brain tumors with minimal time consumption. LSFHS technique is based on preprocessing, segmentation, feature extraction and classification. Input MRI image is preprocessed using adaptive Lee sigma filter in the first hidden layer to minimize noise significantly. In hidden layer 2, gray bimodal histogram segmentation is performed to partition a preprocessed image into a number of segments. Multiple features are extracted from the input image in the third hidden layer. Output layer uses the TanH activation function to match extracted features with disease features for detecting brain tumors. Experimental evaluation is carried out on factors, namely peak signal-to-noise ratio, tumor detection accuracy, error rate and tumor detection with a number of MRI images. The results illustrate LSFHS technique increases tumor detection accuracy by 14% and 25% faster tumor detection time, and reduces the error rate by 58% compared to state-of-the-art works. Qualitative and quantitative results illustrate that our proposed LSFHS technique attains greater performance than state-of-the-art methods. LSFHS technique is designed to detect brain tumors at an earlier stage with higher tumor detection accuracy and less time.

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A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition

Cited by 37 publications

References 43 publications

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review

An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

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