Decision Support Techniques for Dermatology Using Case-Based Reasoning

2020 ◽  
Vol 20 (03) ◽  
pp. 2050024
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Decision Support ◽  
Skin Diseases ◽  
Treatment Plan ◽  
Empirical Evaluation ◽  
Decision Support Techniques ◽  
Skin Lesions ◽  
Data Repository ◽  
Case Based Reasoning ◽  
Hybrid Architecture ◽  
Case Based

Identification of skin disease has become a challenging task with the origination of various skin diseases. This paper presents a case-based reasoning (CBR) decision support system to enhance dermatological diagnosis for rural and remote communities. In this proposed work, an automated way is introduced to deal with the inconsistency problem in CBRs. This new hybrid architecture is to support the diagnosis in multiple skin diseases. The architecture used case-based reasoning terminology facilitates the medical diagnosis. Case based reasoning system retrieves the data which contains symptoms and treatment plan of the disease from the data repository by the way of matching visual contents of the image, such as shape, texture, and color descriptors. The extracted feature vector is fed into a framework to retrieve the data. The results proved using ROC curve that the proposed architecture yields high contribution to the computer-aided diagnosis of skin lesions. In experimental analysis, the system yields a specificity of 95.25% and a sensitivity of 86.77%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works.

CBI + R: A Fusion Approach to Assist Dermatological Diagnoses

2021 ◽  
pp. 2150005
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Skin Diseases ◽  
Treatment Plan ◽  
Diagnostic System ◽  
Data Repository ◽  
Case Based Reasoning ◽  
Advanced Technologies ◽  
Color Features ◽  
Art Works ◽  
Fusion Approach ◽  
Digital Dermoscopy

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI [Formula: see text] R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

scholarly journals Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure

2021 ◽  
Vol 4 (4) ◽  
pp. 73
Author(s):  
Igor Glukhikh ◽  
Dmitry Glukhikh
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Environmental Influence ◽  
Case Based Reasoning ◽  
Technological Object ◽  
Technological Facility ◽  
High Level ◽  
Case Based

The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.

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