A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady, Shakhawan H.; Yousif, Raghad Zuhair; Hasan, Haswadi

doi:10.1155/2020/8125392

Cited by 18 publications

(14 citation statements)

References 54 publications

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“…Step IX: End. From Table 6 and Equations ( 23) and (24), SVN-IS and SVN-AIS are determined. The resultant ranking of EVCS sites by SVN-TOPSIS 67 is presented in Table 11.…”

Section: Comparison and Discussionmentioning

confidence: 99%

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Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Mishra¹,

Rani

Saha

2021

Int J Intell Syst

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Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision-making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single-valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi-criteria decision-making (MCDM) applications. Thus, the objective of this study is to introduce novel single-valued neutrosophic information-based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single-valued subjective and objective weighted integrated approach (SVN-SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure-based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs.To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single-valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa-Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa-Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub-criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications. K E Y W O R D Sadditive ratio assessment, electric vehicle charging station, multi-criteria decision-making, similarity measure, single-valued neutrosophic set | INTRODUCTIONDue to the rapid urbanization and depletion of natural resources, environmental degradation is a vital issue in today's world. 1 Rapid growth in population, urban development, modernization of agriculture, rapid industrialization, multiplicity of the means of transport, and deforestation are main causes of the environmental degradation. Energy is currently a crucial constraint on transport and transport is a foremost determinant of energy demand. To reduce air pollution and protect the environment, several countries have implemented different strategies to promote electric vehicle (EV) production. 2 EV is regarded as an innovative eco-friendly means of transportation which encourages the secu...

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“…Step IX: End. From Table 6 and Equations ( 23) and (24), SVN-IS and SVN-AIS are determined. The resultant ranking of EVCS sites by SVN-TOPSIS 67 is presented in Table 11.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Khan et al 23 proposed`an innovative dispersion control chart based on neutrosophic mean deviation and discussed its enviable properties. Wady et al 24 suggested neutrosophy-based brain tumor intelligent screening system for statistical texture features extraction.…”

mentioning

confidence: 99%

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Mishra¹,

Rani

Saha

2021

Int J Intell Syst

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“…This finding indicated that the degree of homogeneity in an oligodendroglioma is lower compared with an astrocytoma ( 45 ). Besides, features belonging to Gray Level Run Length are often applied to distinguish malignant and benign brain tumors ( 46 ). In our model, these features (Long Run High Gray Level Emphasis and Short Run Low Gray Level) were crucial for predicting the status of 1p/19q co-deletion due to the difference in prognostic outcomes between oligodendroglioma and astrocytoma ( 1 , 3 ).…”

Section: Discussionmentioning

confidence: 99%

Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas

Fan

Sun

et al. 2021

Front. Oncol.

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PurposeThe present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas.MethodsThis retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status.ResultsSix valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group.ConclusionCombined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

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“…Where 𝑔̅ (𝑚,𝑛) is the local mean window size pixels. The probability that the pixel P(m,n) belongs to indeterminate set 𝐼(𝑚, 𝑛) can be calculated using (11) [ 53]:…”

Section: Alow-bestmentioning

confidence: 99%

“…Finally, the probability that the pixel P(m,n) belongs to background (non-white group) 𝐹(𝑚, 𝑛) can be calculated using eq. ( 13) [ 53]:…”

Section: Alow-bestmentioning

confidence: 99%

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Shaban¹

2021

Preprint

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Breast cancer is one of the most common types of cancer that affects women globally and it is the primary cause of death. Early detection of breast cancer is a vital process that can facilitate appropriate treatment, stop the progression of cancer cells, and reduce morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) are the most popular methods that can be used to detect and classify breast cancer accurately. In this paper, a new strategy for classifying breast cancer using Neutrosophic Techniques (NTs) and machine learning techniques is introduced, which is called Breast Cancer Classification Strategy (BC2S). The proposed BC2S consists of two phases, which are; Data Preprocessing Phase (DP2) and Breast Cancer Classification Phase (BC2P). The main aim of the data preprocessing phase is to; (i) extract features from mammogram images and then remove the outlier items, (ii) select the most effective and informative features from those extracted features using new feature selection method called Efficient Ant Colony Optimization (EACO), and (iii) convert the selected features from classical domain into neutrosophic domain using NTs to give accurate classification through the next classification phase BC2P. The proposed classification model uses Deep Neural Network (DNN) to determine whether the patient is normal or infected with benign or malignant cancer. According to experimental results, the proposed strategy outperforms other competitors in terms of accuracy, precision, recall, and F-measure.

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A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Cited by 18 publications

References 54 publications

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

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