A comprehensive review on brain tumor segmentation and classification of MRI images
CS Rao, K Karunakara - Multimedia Tools and Applications, 2021 - Springer
In the analysis of medical images, one of the challenging tasks is the recognition of brain
tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to …
tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to …
[HTML][HTML] Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal
To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
Application of feature extraction for breast cancer using one order statistic, GLCM, GLRLM, and GLDM
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 …
attention. Several techniques of Computer Aided Diagnosis System have been proposed as …
Common spatial pattern in frequency domain for feature extraction and classification of multichannel EEG signals
The extraction methodology of the significant features from the signals is one of the most
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …
Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models
Brain tumors are among the most fatal and devastating diseases, and they often result in a
significant reduction in life expectancy. The devising of treatment plans that can extend the …
significant reduction in life expectancy. The devising of treatment plans that can extend the …
Parametric active contour model-based tumor area segmentation from brain mri images using minimum initial points
MM Islam, MA Kashem - Iran Journal of Computer Science, 2021 - Springer
Accurate brain tumor segmentation from magnetic resonance imaging (MRI) images is
important for proper medication. Manual segmentation may be erroneous and a computer …
important for proper medication. Manual segmentation may be erroneous and a computer …
[PDF][PDF] A novel hybrid method for segmentation and analysis of brain MRI for tumor diagnosis
It is difficult to accurately segment brain MRI in the complex structures of brain tumors,
blurred borders, and external variables such as noise. Much research in developing as well …
blurred borders, and external variables such as noise. Much research in developing as well …
Frequency domain approach in CSP based feature extraction for EEG signal classification
EEG signal in the time domain with high sampled rate faces difficulties for their noise
sensitive properties that lead to erroneous feature extraction. Since the feature extraction is …
sensitive properties that lead to erroneous feature extraction. Since the feature extraction is …
Optimal Superpixel Kernel‐Based Kernel Low‐Rank and Sparsity Representation for Brain Tumour Segmentation
T Ge, T Zhan, Q Li, S Mu - Computational Intelligence and …, 2022 - Wiley Online Library
Given the need for quantitative measurement and 3D visualisation of brain tumours, more
and more attention has been paid to the automatic segmentation of tumour regions from …
and more attention has been paid to the automatic segmentation of tumour regions from …
Detection of effective temporal window for classification of motor imagery events from prefrontal hemodynamics
Motor imagery events classification is very important for functional near-infrared
spectroscopy (fNIRS) based brain-computer interface research. Moreover, the detection of …
spectroscopy (fNIRS) based brain-computer interface research. Moreover, the detection of …