A Novel and Efficient Neuro-Fuzzy Classifier for Medical Diagnosis

2006 ◽  
Author(s):  
Chin-Ming Hong ◽  
Chih-Ming Chen ◽  
Shyuan-Yi Chen ◽  
Chao-Yen Huang
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy

Comparison of deep learning techniques on content based image retrieval

2019 ◽  
pp. 1950285 ◽  
Author(s):  
Meenakshi Garg ◽  
Manisha Malhotra ◽  
Harpal Singh
Keyword(s):  
Image Retrieval ◽  
Medical Diagnosis ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Fuzzy Classification ◽  
Content Based Image Retrieval ◽  
Large Database ◽  
Fuzzy Classifier ◽  
Learning Techniques ◽  
Neuro Fuzzy

Content based Image Retrieval is mostly utilized to extract the pictures from large database. CBIR, which is additionally known as “Query by image” is a technology allowing organizing the computerized Images by their visible attributes. Or, in other words, CBIR is a method for retrieving pictures dependent not on annotations or keywords, but dependent on the feature taken out straightly from the pictures database. CBIR systems are dependent on the utilizations of computer vision methods to the image retrieval issue in huge databases. CBIR is technology of recovering the utmost visually identical pictures to a specified query picture from a cluster or database of pictures. It is useful in a lot of areas like Photography to search images from the database, medical diagnosis etc. Physically annotating the pictures by inputting the metadata or keywords in a huge database can be laborious and might not capture the keyword anticipated to define that picture. CBIR supports in recovering identical pictures from an database of pictures deprived of pictures annotation. In this paper, we are compare the Deep Neural Networks and Neuro-Fuzzy Classifier, both have different outcomes and different results to predict the image. The comparison of our proposed methods Neuro-fuzzy classification and deep neural network shows that the improvement in accuracy. The accuracy values 71.6% and 73.4% for DNN and Neuro-Fuzzy Classifier[Formula: see text] method. The visual and qualitative results are presented for validation of the proposed method.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

scholarly journals High-Performance Adaptive Neurofuzzy Classifier with a Parametric Tuning

2018 ◽  
Vol 155 ◽  
pp. 01037
Author(s):  
Sergey Gorbachev ◽  
Vladimir Syryamkin
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Adaptive Algorithms ◽  
Fuzzy Classifier ◽  
Network Adaptation ◽  
Error Back Propagation ◽  
Multicriteria Problems ◽  
Neuro Fuzzy

The article is devoted to research and development of adaptive algorithms for neuro-fuzzy inference when solving multicriteria problems connected with analysis of expert (foresight) data to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative development. The article describes the optimized structuralfunctional scheme of the high-performance adaptive neuro-fuzzy classifier with a logical output, which has such specific features as a block of decision tree-based fuzzy rules and a hybrid algorithm for neural network adaptation of parameters based on the error back-propagation to the root of the decision tree.

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 269-281 ◽  
Author(s):  
Hima Bindu ◽  
Manjunathachari K.
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition System ◽  
Feature Descriptor ◽  
Global Features ◽  
Fuzzy Classifier ◽  
Content Type ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
Proposed Model

Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Speech Enhancement Using Neuro-Fuzzy Classifier

2021 ◽  
pp. 164-181
Author(s):  
Judith Justin ◽  
Vanithamani R.
Keyword(s):  
Feature Extraction ◽  
Speech Enhancement ◽  
The Other ◽  
Objective Measures ◽  
Noise Levels ◽  
Fuzzy Classifier ◽  
Noisy Speech ◽  
Enhancement Technique ◽  
Time Frequency ◽  
Neuro Fuzzy

In this chapter, a speech enhancement technique is implemented using a neuro-fuzzy classifier. Noisy speech sentences from NOIZEUS and AURORA databases are taken for the study. Feature extraction is implemented through modifications in amplitude magnitude spectrograms. A four class neuro-fuzzy classifier splits the noisy speech samples into noise-only part, signal only part, more noise-less signal part, and more signal-less noise part of the time-frequency units. Appropriate weights are applied in the enhancement phase. The enhanced speech sentence is evaluated using objective measures. An analysis of the performance of the Neuro-Fuzzy 4 (NF 4) classifier is done. A comparison of the performance of the classifier with other conventional techniques is done for various noises at different noise levels. It is observed that the numerical values of the measures obtained are better when compared to the others. An overall comparison of the performance of the NF 4 classifier is done and it is inferred that NF4 outperforms the other techniques in speech enhancement.

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