Three-way decision support for diagnosis on focal liver lesions

Chen, Yufei; Yue, Xiaodong; Fujita, Hamido; Fu, Shimin

doi:10.1016/j.knosys.2017.04.008

Cited by 85 publications

(26 citation statements)

References 58 publications

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“…Specificity value for proposed method in three dataset is 97.52%, 97%, and 97.2%. Precision of our method is 0.04% superior to Chen et al, 27 1.33% superior to Abdar et al 26 (A) and 19.61% superior to Abdar et al 26 (B). Figure 5 demonstrates the comparison of precision and F-1 score values that were obtained for proposal and other methods.…”

Section: Performance Comparisonmentioning

confidence: 57%

“…Comparing to other methods, proposed method is 5.29% higher than GA, 12.5% higher than PSO, 5.37% higher ACO, and 9.72% higher than FA. F-1 score of our method is 3.53% superior to Chen et al, 27 1.37% superior to Abdar et al 26 (A) and 20.21% superior to Abdar et al 26 (B). In proposed method, precision value of three dataset is 95.62%, 95.86%, and 95.43% and F-1 score is 96.85%, 96.2%, and 96.5%.…”

Section: Performance Comparisonmentioning

confidence: 63%

“…Sensitivity value for proposed method in three dataset is 95.82%, 95%, and 95.34%. Youden score of our proposed method is 3.50% superior to Chen et al, 27 1.35% superior to Abdar et al 26 (A) and 20.11% superior to Abdar et al 26 (B). Specificity value for proposed method in three dataset is 97.52%, 97%, and 97.2%.…”

Section: Performance Comparisonmentioning

confidence: 69%

“…The connections between all lower layers are directed, with the arrows pointed toward the layer that is closest to the data. Our method is compared with the existing techniques such as, Chen et al, 27 Diamant et al, 28 and Abdar et al 26 In Chen et al 27 Three-way decision support is utilized to the diagnosis of focal liver lesion. The above Figure 4 illustrates the Comparison of accuracy, sensitivity and specificity values that were obtained for proposal and other methods.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Chen et al, 27 designed a three-way decision support for diagnosis on focal liver lesions. Their approach was motivated by three-way decision theory (3WD).…”

Section: Related Workmentioning

confidence: 99%

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Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F-1 score in simulation process when compared with other existing techniques. K E Y W O R D S deep belief network (DBN), grasshopper optimization algorithm (GOA), liver disease classification, principal component analysis (PCA)

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Section: Performance Comparisonmentioning

confidence: 57%

Section: Performance Comparisonmentioning

confidence: 63%

Section: Performance Comparisonmentioning

confidence: 69%

Section: Performance Comparisonmentioning

confidence: 99%

“…Chen et al, 27 designed a three-way decision support for diagnosis on focal liver lesions. Their approach was motivated by three-way decision theory (3WD).…”

Section: Related Workmentioning

confidence: 99%

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Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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q‐Rung orthopair fuzzy sets‐based decision‐theoretic rough sets for three‐way decisions under group decision making

Liang

Cao

2019

Int J Intell Syst

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As an extension of Pythagorean fuzzy sets, the q‐rung orthopair fuzzy sets (q‐ROFSs) can easily solve uncertain information in a broader perspective. Considering the fine property of q‐ROFSs, we introduce q‐ROFSs into decision‐theoretic rough sets (DTRSs) and use it to portray the loss function. According to the Bayesian decision procedure, we further construct a basic model of q‐rung orthopair fuzzy decision‐theoretic rough sets (q‐ROFDTRSs) under the q‐rung orthopair fuzzy environment. At the same time, we design the corresponding method for the deduction of three‐way decisions by utilizing projection‐based distance measures and TOPSIS. Then, we extend q‐ROFDTRSs to adapt the group decision‐making (GDM) scenario. To fuse different experts’ evaluation results, we propose some new aggregation operators of q‐ROFSs by utilizing power average (PA) and power geometric (PG) operators, that is, q‐rung orthopair fuzzy power average, q‐rung orthopair fuzzy power weighted average (q‐ROFPWA), q‐rung orthopair fuzzy power geometric, and q‐rung orthopair fuzzy power weighted geometric (q‐ROFPWG). In addition, with the aid of q‐ROFPWA and q‐ROFPWG, we investigate three‐way decisions with q‐ROFDTRSs under the GDM situation. Finally, we give the example of a rural e‐commence GDM problem to illustrate the application of our proposed method and verify our results by conducting two comparative experiments.

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A new approach to three‐way decisions making based on fractional fuzzy decision‐theoretical rough set

Abdullah

Al-Shomrani

Лю

et al. 2021

Int J of Intelligent Sys

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The main aim of the proposed work is to develop the new technique based on decision‐theoretical rough sets (DTRSs) and their applications in three‐way decision‐making problems. This study first develop a fractional fuzzy set (FFS) and their operations, the FFS is a more generalized and accurate tool for describing uncertainty in real‐life data information. A new form of decision technique for dealing with the issue of choice based on DTRSs is included in the three‐way decisions. The loss function of DTRSs is being used in the proposed decision method model. Initially, the idea of fractional fuzzy α‐covering (FF α‐covering), fractional fuzzy α–neighborhood (FF α–neighborhood) was introduced. Under the fractional fuzzy state, we integrated the loss function of DTRSs with covering‐based fractional fuzzy rough sets. Furthermore, we proposed and established performance characteristics for a new fractional fuzzy α‐covering decision‐theoretical rough sets model (FFCDTRSs). Then, according to the level of fractional fuzzy numbers (FFN's) positive and negative membership and related three‐way decision‐making, four methods to solve the expected loss expressed in the form of (FFNs) are described. We have developed a multicriteria decision algorithm (MCDM) based on FFCDTRS. Then an example is used to prove the feasibility of the four methods to solve the MCDM problem. Finally, the results of four distinct decision procedures with various loss functions are compared. The proposed three‐way decision‐making models are more accurate as compared with particular fuzzy sets.

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Three-way decision support for diagnosis on focal liver lesions

Cited by 85 publications

References 58 publications

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

q‐Rung orthopair fuzzy sets‐based decision‐theoretic rough sets for three‐way decisions under group decision making

A new approach to three‐way decisions making based on fractional fuzzy decision‐theoretical rough set

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