Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

Zarandi, M. H. Fazel; Zarinbal, Marzieh; Izadi, Mohammad

doi:10.1016/j.asoc.2009.11.019

Cited by 107 publications

(66 citation statements)

References 35 publications

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“…[91][92][93][94][95] Researchers have addressed the issues and systems to predict the risks in terms of anticipating critical parameters for control, enhance control strategies to diagnose diseases and diagnosis regarding disorders of specific organs. [96][97][98][99] Medical advisory systems for deciding the quantum of medicine, taking online measurements, monitoring, controlling of parameters are proposed. [100][101][102] Computer aided medical diagnosis, medical image processing, noise reduction in medical images, simulation and automated generation of fuzzy models and expert systems using generic methodology are the areas that alleviated FES to the new height and could provide a way of generating solutions based on stored data.…”

Section: Methodologies and Modelling Of Fuzzy Expert Systemsmentioning

confidence: 99%

Fuzzy Expert Systems (FES) for Medical Diagnosis

Sikchi¹,

Sikchi²,

Ali³

2013

IJCA

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Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems based on the expert's knowledge and observations. This paper reviews the trend in development of FES and application potential over past two and half decades in the medical field, based on the references of 173 articles from 124 journals, several proceedings and web media. In order to investigate the significance of FES for medical diagnosis, the articles are classified into five distinct categories: Reviews and Surveys on Fuzzy Expert Systems in Medical Diagnosis, Applications of Fuzzy Expert Systems in Medical Diagnosis, Methodologies and Modelling of Fuzzy Expert Systems, Neuro-Fuzzy Approaches, Fuzzy Expert System Shells and Frameworks. The development of disease specific applications using FES is observed to be the area of most significant interest of the researchers. The earlier contributions are reviewed, classified, analyzed and suggested the future scope.

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Section: Methodologies and Modelling Of Fuzzy Expert Systemsmentioning

confidence: 99%

Fuzzy Expert Systems (FES) for Medical Diagnosis

Sikchi¹,

Sikchi²,

Ali³

2013

IJCA

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“…Most of the method basing on fuzzy logic adopt Type-1 fuzzy sets representing uncertainties with the range between [0,1] and type-1 fuzzy sets is having a precise membership function where its elements are real number. To handle [28] this difficulties a type-2 fuzzy sets is introduced which is most able to handle the uncertainty related to noisy and non-stationary than type-1 fuzzy set along with allowing uncertainty [29][30][31][32] to its associated membership degree. For the prediction of stock price Chih-Feng et al presented a type-2 neuro-fuzzy model where [28] a self constructed clustering method designed the type-2 fuzzy rules and then refined it by a hybrid algorithm.…”

Section: Psomentioning

confidence: 99%

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Das¹,

Mishra²,

Rout³

2017

JESTR

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Financial forecasting is one of the imperative fields of research, where investors invest money and get restless for the future changes of the stock values in the market. In the recent course of time forecasting stock price is one of the challenging tasks. To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc. With the development of ANNs, investors are hoping the best prediction because networks have great capability of machine learning problems such as classification and prediction. ANNs particularly Back Propagation (BP) has overlooked the non-stationary and noise characteristics of stock market data, as the training of BP is intricate due to the noise data and the network fall into a natural solution such as always predict an usual output. Most optimization techniques have been used for training the weights of forecasting models. Since no single optimization technique is invariably superior to others. Recently various nature inspired optimization techniques have been introduced and successfully employed to many fields of Financial Engineering. This survey aims to study the mostly used Bio-inspired optimization techniques on Stock Market Prediction and makes a comparative analysis between them.

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“…Some examples of FRBS use in MRI have been reported [15] [16], and two studies, conducted by Forkert et al [17][18], have described FRBS application to solve the problem of segmenting vasculature in MR images. However, Forkert's work focuses on 3D time-of-flight (TOF) magnetic resonance angiography (MRA) rather than magnetic resonance venography with SWI.…”

Section: Introductionmentioning

confidence: 99%

Application of a Mamdani-Type Fuzzy Rule-Based System to Segment Periventricular Cerebral Veins in Susceptibility-Weighted Images

Aymerich

Sobrevilla

Montseny

2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients.

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Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

Cited by 107 publications

References 35 publications

Fuzzy Expert Systems (FES) for Medical Diagnosis

Fuzzy Expert Systems (FES) for Medical Diagnosis

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Application of a Mamdani-Type Fuzzy Rule-Based System to Segment Periventricular Cerebral Veins in Susceptibility-Weighted Images

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