HE Stain Image Segmentation Using an Innovative Type-2 Fuzzy Set-Based Approach

2019 ◽  
pp. 276-299 ◽  
Author(s):  
Dibya Jyoti Bora
Keyword(s):  
Fuzzy Set ◽  
Gold Standard ◽  
Medical Diagnosis ◽  
Clustering Techniques ◽  
The Status ◽  
Patient Segmentation ◽  
Hard Clustering ◽  
High Level ◽  
Proper Analysis

HE stain images are widely used in medical diagnosis and often considered a gold standard for histology and pathology laboratories. A proper analysis is needed to have a critical decision about the status of the diagnosis of the concerned patient. Segmentation is always considered as an advanced stage of image analysis where objects of similar properties are put in one segment. But segmentation of HE stain images is not an easy task as these images involve a high level of fuzziness with them mainly along the boundary edges. So, traditional techniques like hard clustering techniques are not suitable for segmenting these images. So, a new approach is proposed in this chapter to deal with this problem. The proposed approach is based on type-2 fuzzy set and is new. The experimental results prove the superiority of the proposed technique.

scholarly journals Fuzzy logics and medical diagnosis of nursing assessment

2015 ◽  
Vol 49 (4) ◽  
pp. 271-274
Author(s):  
U Dev ◽  
A Sultana ◽  
NK Mitra
Keyword(s):  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Fuzzy Relation ◽  
Fuzzy Logics ◽  
Nursing Assessment ◽  
Fuzzy Methods ◽  
Application Systems ◽  
Diagnosis And Classification ◽  
Set Aggregation

This paper argues that fuzzy representations are appropriate in applications where there are major sources of imprecision and / or uncertainty. Case studies of fuzzy approaches to specific problems of medical diagnosis and classification are described in support of this argument. The solutions use a variety of fuzzy methods including clustering, fuzzy set aggregation and type- 2 fuzzy set and Type-2 fuzzy relation modeling of linguistic approximations. It is concluded that the fuzzy approach to the development of artificial intelligence in application systems is beneficial in these contexts because of the need to focus on uncertainty as a main issue. DOI: http://dx.doi.org/10.3329/bjsir.v49i4.22631 Bangladesh J. Sci. Ind. Res. 49(4), 271-274, 2014

Response to DDAVP in children with von Willebrand disease type 2

2009 ◽  
Vol 29 (02) ◽  
pp. 143-148 ◽  
Author(s):  
U. Budde ◽  
K. Beutel ◽  
W.-A. Hassenpflug ◽  
H. Hauch ◽  
T. Obser ◽  
...  
Keyword(s):  
Disease Type ◽  
Von Willebrand Disease ◽  
Molecular Defect ◽  
Variable Response ◽  
Functional Parameters ◽  
Functional Defect ◽  
Biologic Response ◽  
High Level ◽  
Von Willebrand

SummaryWe have prospectively evaluated the biologic response to desmopressin (DDAVP) in 28 children with type 2 von Willebrand disease (VWD) in correlation with the phenotype and the molecular defect of VWF. The diagnosis of VWD type 2 was mainly based on VWF functional parameters and/or an aberrant VWF multimer pattern. Seventeen different mutations were identified (6 of them novel). No response with respect to the functional parameters VWF:RCo and/or VWF:CB was seen in patients with severe abnormality of the VWF multimer pattern. One patient with VWD type 2A phenotype IIC Miami did not respond with respect to VWF:CB, but showed a good response of VWF:Ag and FVIII:C as expected. Interestingly he showed a persistently high level of VWF:Ag and FVIII:C up to 4 hours after DDAVP infusion. Patients with minor alterations of multimer structure and particular mutations responded well to DDAVP, whereas patients with normal multimer structure but a defect in platelet dependent functional parameters did not respond with VWF:RCo. Conclusion: Children with VWD type 2 show a variable response to desmopressin depending on the mutation that correlates with the functional defect and the presence or absence as well as the half-life of large VWF multimers. Our data emphasize the usefulness of DDAVP testing even in patients with VWD type 2, possibly with the exception of VWD type 2B.

Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Fuzzy Set ◽  
Supplier Selection ◽  
Symbolic Model ◽  
Green Supplier Selection ◽  
Linguistic Term ◽  
Interval Type ◽  
Decision Making Model

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

Deep AI Enabled Ubiquitous Wireless Sensing

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Chenning Li ◽  
Zhichao Cao ◽  
Yunhao Liu
Keyword(s):  
Great Success ◽  
Wireless Sensing ◽  
Ubiquitous Sensing ◽  
Sensing Systems ◽  
The Status ◽  
Sensing Model ◽  
High Level ◽  
Traditional Approaches ◽  
The Internet Of Things ◽  
High Level Feature

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

The status of frailty in poor older adults with type 2 diabetes mellitus or hypertension: the case of Mexico

2019 ◽  
Vol 40 (2) ◽  
pp. 303-309
Author(s):  
Javier E. García-de-Alba-García ◽  
Ana L. Salcedo-Rocha ◽  
Susana De-la-Rosa-Hernández
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Older Adults ◽  
Type 2 Diabetes Mellitus ◽  
The Status

scholarly journals Generalised Interval-Valued Fuzzy Soft Set

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Shawkat Alkhazaleh ◽  
Abdul Razak Salleh
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Decision Making Problem ◽  
Fuzzy Soft Sets ◽  
Interval Valued

We introduce the concept of generalised interval-valued fuzzy soft set and its operations and study some of their properties. We give applications of this theory in solving a decision making problem. We also introduce a similarity measure of two generalised interval-valued fuzzy soft sets and discuss its application in a medical diagnosis problem: fuzzy set; soft set; fuzzy soft set; generalised fuzzy soft set; generalised interval-valued fuzzy soft set; interval-valued fuzzy set; interval-valued fuzzy soft set.

Consistently High Level of Cardiorespiratory Fitness and Incidence of Type 2 Diabetes

2017 ◽  
Vol 49 (10) ◽  
pp. 2048-2055 ◽  
Author(s):  
HARUKI MOMMA ◽  
SUSUMU S. SAWADA ◽  
I-MIN LEE ◽  
YUKO GANDO ◽  
RYOKO KAWAKAMI ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiorespiratory Fitness ◽  
High Level

Multi-criteria decision making and pattern recognition based on similarity measures for Fermatean fuzzy sets

2021 ◽  
pp. 1-17
Author(s):  
Changlin Xu ◽  
Juhong Shen
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Set ◽  
Similarity Measures ◽  
New Method ◽  
Fuzzy Decision Making ◽  
Multi Criteria Decision Making

 Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. Fermatean fuzzy set proposed by Senapati and Yager, which can easily process uncertain information in decision making, pattern recognition, medical diagnosis et al., is extension of intuitionistic fuzzy set and Pythagorean fuzzy set by relaxing the restraint conditions of the support for degrees and support against degrees. In this paper, we focus on the similarity measures of Fermatean fuzzy sets. The definitions of the Fermatean fuzzy sets similarity measures and its weighted similarity measures on discrete and continuous universes are given in turn. Then, the basic properties of the presented similarity measures are discussed. Afterward, a decision-making process under the Fermatean fuzzy environment based on TOPSIS method is established, and a new method based on the proposed Fermatean fuzzy sets similarity measures is designed to solve the problems of medical diagnosis. Ultimately, an interpretative multi-criteria decision making example and two medical diagnosis examples are provided to demonstrate the viability and effectiveness of the proposed method. Through comparing the different methods in the multi-criteria decision making and the medical diagnosis application, it is found that the new method is as efficient as the other methods. These results illustrate that the proposed method is practical in dealing with the decision making problems and medical diagnosis problems.

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