Soft computing in medicine

Yardımcı, Ahmet

doi:10.1016/j.asoc.2009.02.003

Cited by 104 publications

(43 citation statements)

References 67 publications

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“…Although ANNs are widely spread, they have a series of problems related to: i) limited theory in assisting the topology design; ii) finding an acceptable solution to a problem is not guaranteed; iii) the rationalization of the solutions provided is difficult [5]. In addition, the wrong application of the training algorithms can lead to overfitting and lack of robustness, factors which negatively influence the network's performance.…”

Section: Introductionmentioning

confidence: 97%

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Curteanu

Drăgoi

Dafinescu

2015

Advances in Computational Intelligence

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Abstract.A modelling procedure based on hybrid configuration composed of artificial neural networks, differential evolution and clonal selection algorithms is developed and applied in this work. The neural network represents the model of the system, while the differential evolution and clonal selection algorithms perform a simultaneous topological and parametric optimization of the model. The results indicated that the combination of the two optimizers produces better results compared with each of them working separately. As case study, styrene polymerization, a complex process which is difficult to model when taking into consideration all the internal interactions, was chosen. Neural networks, designed in an optimal form, proved to be adequate tools for modelling this system.

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Section: Introductionmentioning

confidence: 97%

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Curteanu

Drăgoi

Dafinescu

2015

Advances in Computational Intelligence

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“…Usefulness of K-means clustering algorithm techniques was applied to predict classification, accuracy, and sensitivity versus specificity was evaluated using ROC analysis procedure. Further, the author suggested exploring a colour-based edge segmentation technique using derivative operators to all chromatic channels and by using Bayesian decision theory [2]. N. Walia et al (2015) had presented a systematic approach for design and identification of tuberculosis using fuzzy based decision support system.…”

Section: Forero Et Al (2004)mentioning

confidence: 99%

“…Detection of mycobacterium tuberculosis organism at initial stages is very important in order to prevent its growth and maintain world's population [22]. According to the studies conducted by World Health organization a third part of world population (1722 million people) are carriers of these bacteria, originating 10 million new cases of active TB worldwide and approximately 3 million death annually [2]. Pulmonary TB is a contagious bacterial infection that involves lungs and can infect other organs or tissues such as a brain, kidneys, bone, and skin.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Tuberculosis Diagnosability

Walia¹,

Singh²,

Tiwari³

et al. 2015

IJSC

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In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done by comparing expert knowledge and system generated response. This basic emblematic approach using fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and provides support platform to pulmonary researchers in identifying the ailment effectively. KEYWORDSExpert system, fuzzy diagnosability, rulebased method, MATLAB, Tuberculosis (TB).

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“…Artificial intelligence methods have shown good applicability to pattern recognition issues with uncertainty models [12][13][14] and complex data [15,16]. Presently, a number of intelligent approaches including expert systems, artificial neural networks, and genetic algorithms are used in medical diagnosing and treatment [17]. For example, a fuzzy method was built for tremor assessment, prediction, and rehabilitation [18], a reasoning method was employed to construct medical expert systems [19], and a new heart disease diagnosis expert system has been established [20].…”

Section: Introductionmentioning

confidence: 99%

A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis

Sangwoo

Gao

Xie

et al. 2017

Computer Assisted Surgery

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Traditional dysphagia prescreening diagnostic methods require doctors specialists to give patients a total score based on a water swallow test scale. This method is limited by the high dimensionality of the diagnostic elements in the water swallow test scale with heavy workload (Towards each patient, the scale requires the doctors give score for 18 diagnostic elements respectively) as well as the difficulties of extracting and using the diagnostic scale data's non-linear features and hidden expertise information (Even with the scale scores, specific diagnostic conclusions are still given by expert doctors under the expertise). In this paper, a hybrid classifier model based on Nonlinear-Principal Component Analysis (NPCA) and Deep Belief Networks (DBN) is proposed in order to effectively extract the diagnostic scale data's nonlinear features and hidden information and to provide the key scale elements' locating methods towards the diagnostic results (The key scale elements that affect different diagnostic conclusions are given to improve the efficiency and pertinence of diagnosis and reduce the workload of diagnosis). We demonstrate the effectiveness of the proposed method using the frame of 'information entropy theory'. Real dysphagia diagnosis examples from the China-Japanese Friendship Hospital are used to demonstrate applications of the proposed methods. The examples show satisfactory results compared to the traditional classifier.

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Soft computing in medicine

Cited by 104 publications

References 67 publications

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

A Decision Support System for Tuberculosis Diagnosability

A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis

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