Linguistic recognition system for identification of some possible genes mediating the development of lung adenocarcinoma

De, Rajat K.; Ghosh, Anupam

doi:10.1016/j.inffus.2008.11.007

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…The conjunctive, disjunctive and compromise properties of the fuzzy logic have been widely explored in image processing and have proved to be useful in image fusion. The fuzzy logic is applied both as a feature transform operator or a decision operator for image fusion [47,51,48,52,53,49,54,55,50,56,41,57,58,60,88,59,87,89,90,91,43,82,92]. There are several applications of fuzzy logic base image fusion such as brain diagnosis [47,48,49,50], cancer treatment [51], image segmentation and integration [51,52], maximization mutual information [53], deep brain stimulation [54], brain tumor segmentation [55], image retrieval [56,57], spatial weighted entropy [56], feature fusion [56], multimodal image fusion [41,58,59], ovarian cancer diagnosis [60], sensor fusion [88], natural computing methods [87] and gene expression [89,…”

Section: Methods Based On Fuzzy Logicmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Methods Based On Fuzzy Logicmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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“…The fuzzy logic is applied both as a feature transform operator or a decision operator for image fusion [47][48][49][50][51][52][53][54][55][56][57][58][59][60], [87][88][89][90][91][92], [82]. There are several applications of fuzzy logic base image fusion such as brain diagnosis [47,48,49,50], cancer treatment [51], image segmentation and integration [51][52], maximization mutual information [53], deep brain stimulation [54], brain tumor segmentation [55], image retrieval [56][57], spatial weighted entropy [56], feature fusion [56], multimodal image fusion [41,58,59], ovarian cancer diagnosis [60], sensor fusion [88], natural computing methods [87] and gene expression [89,90].…”

Section: E Fuzzy Logic Based Methodsmentioning

confidence: 99%

A Survey on Multimodal Medical Image Fusion

Devanna¹,

Kumar²

2017

IOSR JCE

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“…These ensemble techniques (averaging, voting, blending, bagging, and gradient boosting) have the advantage to alleviate the different sizes of interrelated test and training samples. Ensemble learning exhibits potential performance for improving the accuracy in classifying data under the different crisp and fuzzy sets 40,41 of environments. Moreover, it mostly outperforms compare to the individual classifiers.…”

Section: Basic Terminologies and Materialsmentioning

confidence: 99%

MEM‐FET: Essential protein prediction using membership feature and machine learning approach

Payra,

Saha,

Ghosh

2023

Proteins

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Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day‐to‐day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM‐FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM‐FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM‐FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

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Linguistic recognition system for identification of some possible genes mediating the development of lung adenocarcinoma

Cited by 9 publications

References 51 publications

Medical image fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

A Survey on Multimodal Medical Image Fusion

MEM‐FET: Essential protein prediction using membership feature and machine learning approach

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