A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors

Zarinbal, Marzieh; Zarandi, M. H. Fazel; Türkşen, I.B.; Izadi, Mohammad

doi:10.1007/s10916-015-0311-6

Cited by 27 publications

(7 citation statements)

References 40 publications

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“…First, by [19,21,28], such that [19] and [21] both are direct applications to a humanoid robot vision system, although both focused the use of IT2 FLS in different ways, as the first was used for object sample selection and the later was used for feature validity; and [28] is an application in auroral image segmentation. Secondly, the rest of the papers focused their algorithms to specifically solve medical imaging databases of different natures [18,20,22,27,29,31,33], using image segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…It is also worth noting that, although most research papers used variations of the IT2 FCM clustering algorithm, some papers solely used IT2 FLSs as some type of expert system [20,23,27,28,31]. Some even used hybrid versions of IT2 FLS or IT2 FCM, such as Interval-Valued Possibilistic Fuzzy C-Means (IVPFCM) [7] and Extended Type-2 Fuzzy Information Theory (ET2FIT) [21].…”

Section: Discussionmentioning

confidence: 99%

“…In M. Zarinbal et al (2015) [31], an IT2 fuzzy automated tumor detection system was proposed, where the image quality of MRI scans is enhanced via preprocessing, segmentation and feature extraction, such that multiple consecutive planes are processed to improve accuracy. Evaluation of this approach was done with MRI scans, where astrocytomas were differentiated in order to assess the quality of the proposed method.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…They showed that the high uncertainty of some clinical symptoms required more accuracy to get acceptable results. Zarinbal et al [19,20] developed a type-2 fuzzy image processing expert system to diagnose brain tumors. They evaluated the system performance using 95 MRI scans, showing good capacity of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

et al. 2018

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The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in ve categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

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“…They mimic the decision-making abilities of a human expert, and they are designed to solve complex problems by reasoning. Expert system applications include, among others, medicine [29,53,60], diagnosis and control of power systems [26,27], evaluation of journal grades [61], information systems investment evaluation [19], transport management [31,51], industry [14,38] and sport [12,15,32,33,39].…”

Section: Introductionmentioning

confidence: 99%

A web-oriented expert system for planning hurdles race training programmes

Przednówek

Wiktorowicz

Krzeszowski

et al. 2018

Neural Comput & Applic

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This paper presents a web-oriented expert system, named iHurdling, to predict results and generate training loads for 110 and 400 m hurdles races. The database contains 40 annual training programmes for the 110 m hurdles and 48 programmes for the 400 m hurdles. The predictive models include linear regressions in the form of ordinary least squares, ridge, LASSO, elastic net and nonlinear models in the form of a radial basis function neural network and fuzzy rule-based system. The leave-one-out cross-validation method is used to compare, and choose the best model. It shows that the proposed fuzzy-based model has the lowest validation error. The developed web application can support a coach in planning training programmes for hurdles races. It allows the athlete's results to be predicted and can generate training loads for an athlete, selected from database. The application can be run on a computer or a mobile device. The system was implemented using the R programming language with the Shiny framework and additional packages. The limitations of the presented approach are related to the lack of consideration of an athlete's physiological and psychological parameters, but the generated training programs might be used as a suggestion for the coach.

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A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors

Cited by 27 publications

References 40 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

A web-oriented expert system for planning hurdles race training programmes

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