An Advanced Algorithm Combining SVM and ANN Classifiers to Categorize Tumor with Position from Brain MRI Images

Ahmmed, Rasel; Rahman, Md. Asadur; Hossain, Md. Foisal

doi:10.25046/aj030205

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…ANN is a prominent classifier for EEG signal classification in supervised learning technique. In ANN, know features of different data class are fed and trained it to make a predictive model to classify the unknown data feature [40]. According to the feature size and class number, the structure of ANN is set to train the network with some suitable hidden layers.…”

Section: H Alertness Classification Methodology Using Annmentioning

confidence: 99%

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Rahman¹,

Rashid²,

Khanam³

et al. 2019

ijacsa

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Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

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Section: H Alertness Classification Methodology Using Annmentioning

confidence: 99%

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Rahman¹,

Rashid²,

Khanam³

et al. 2019

ijacsa

Self Cite

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“…Each small processing unit in the hidden layer calculates the appropriate weight of the signal and provide output [38][39][40] as Γ given in the following equation:…”

Section: Classificationmentioning

confidence: 99%

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

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The extraction methodology of the significant features from the signals is one of the most important prerequisite steps for EEG signal classification. Common spatial pattern (CSP) is a widely used feature extraction method for EEG signal but with a lacking of failing to maintain discriminative features between classes in the time domain, and further as a consequence, ends up in inconvenience with erroneous output. To overcome the limitations of the convention CSP, this research work proposes a novel frequency domain CSP (FCSP) method for feature extraction. This method proposes to convert the time domain EEG signal to its power spectral density (PSD) so that the event-related variation can be found in the frequency domain. After that, the CSP method is applied to the PSD values of the selected channels to extract the variation based on the spatial pattern of the channels for the events. The output of this method helps to extract simple features from the FCSP-PSD data for the classification. The proposed method is applied to motor imagery data from BCI competition IV. To check the applicability of the proposed method, a complex environment was created considering the same lobe events such as combined left and right feet (Class#1) versus right-hand (Class#2) imagery movement. To compare the performance of the proposed work, the method is also applied to the conventional classification problem (left-hand vs right-hand imagery movement) and found very promising results of 91% accuracy on average.

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“…This study used the ECOC approach since the way how these approach works are to add redundant data into messages which are sent in the form of a codeword. Thus, the message recipient can detect errors in the messages and recover the original message if there are several small errors [14], [15], [27]. Furthermore, the ECOC SVM approach is very suitable for several noise data that has become a common problem of breast cancer classification Kernel SVM…”

Section: Support Vector Machinementioning

confidence: 99%

Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

Novitasari¹,

Lubab²,

Sawiji³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The increasing number of breast cancer in recent years has attracted numerous researchers' attention. Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer. The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer. In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image. Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics. The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification. The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.

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An Advanced Algorithm Combining SVM and ANN Classifiers to Categorize Tumor with Position from Brain MRI Images

Cited by 16 publications

References 12 publications

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

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