Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

Starkey, Andrew; Ahmad, Aliyu Usman; Hamdoun, Hassan

doi:10.1088/1757-899x/261/1/012006

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Due to the high cost of processing, the feature selection algorithm needed improvement. Accurate features are needed to assist in the segmentation of tumors [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

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Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

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Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

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“…Due to the high cost of processing, the feature selection algorithm needed improvement. Accurate features are needed to assist in the segmentation of tumors [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…With the SVM feature classification, the tumorous and nontumorous images were identified [ 25 ]. In [ 26 ], SOM was improved with its weights, and therefore its capabilities were improved for relevant feature selection. Furthermore, weighted SOM was improved at this level, and it did not require preprocessing.…”

Section: Related Workmentioning

confidence: 99%

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…In this study, Dataset is extracted using DWT, features are reduced with using PCA and classified through four SVM classifier and classification accuracy is 66.6 percent [16]. In this study, Feature weighted selforganization map is proposed , algorithm performs feature selection, it's initial accuracy of feature classification is 60percent and it is extending [17]. In this stud, predicted pre particle swarm optimization is proposed with single hidden neural network layer and classification accuracy for brain tumor is 97 percent [18].…”

Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element.. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

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Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data

Onay

Köse

2019

Biomedical Engineering / Biomedizinische Technik

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The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.

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Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

Cited by 3 publications

References 8 publications

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data

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