Adaptive Neuro-fuzzy Inference System Classifier Research Articles

Breast Tumors Classification Using Adaptive Neuro-Fuzzy Inference System

Breast cancer is one of the world's leading causes of cancer-related deaths and ranks second in the cancer fact sheets. In Sudan, the increasing incidence, detection at late stages, and early onset of the disease make early detection and diagnosis of breast cancer an overbearing task. The objective of this study was to create a computer interfacing system for the localization, detection, and classification of breast masses using adaptive neuro-fuzzy inference system (ANFIS). The ANFIS classifier was used to detect the breast cancer when 5 features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy-logic qualitative approach. Results demonstrated that the proposed methodologies have high potential in enhancing breast images and localizing, detecting, and classifying the breast tumor. The system was able to achieve an accuracy of 94.4% sensitivity, 100% specificity, 97.1% positive predictive value, 100% negative predictive value, an Az value of .972, and an overall classification accuracy of 98%.

Comparative Analyses of Classifiers for Diagnosis of Skin Cancer using Dermoscopic Images

In recent years one of the emerging deadliest diseases is Skin cancer. Skin cancers are of different types. But melanoma, basal cell carcinoma and squamous cell carcinoma these types are most commonly found in humans. The death rate due to skin lesions can be reduced if detected early. An efficient image analysis module has been developed with efficient algorithm to detect the skin lesions. In the analysis system classification plays an important role in identification of defect. In the proposed system different types of classifiers such as Support Vector Machine, ensemble classifier, probabilistic neural network and adaptive neuro-fuzzy inference system classifiers are used in the classification process and their performance is compared and the classifier with best performance is used in identifying the skin lesions. Keywords: Classification, Ensemble, Image Segmentation, Neural Network, Neuro-Fuzzy, Skin Cancer

Liver Cancer Identification using Adaptive Neuro-Fuzzy Inference System

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of liver tumor as benign or malignant by analyzing CT liver images. Decision making was performed in four stages: in the first stage, image is enhanced to improve its quality. In the second stage, the liver is extracted based on thresholding and boundary extraction algorithms. Then it is given as input to Fuzzy C-mean (FCM) clustering algorithm to segment it's inside tumor object. In the third stage, texture features and Discrete Wavelet Transformation features are extracted. In the fourth stage, the ANFIS classifier is trained by these extracted features using the backpropagation gradient descent method in combination with the least squares method. To evaluate the effect of each type of features on the tumor classification process, these two sets of features are trained separately to take the right decision to classify the liver tumor as malignant or benign. The performance of the proposed approach was tested and evaluated using a group of patient's CT images and the experimental results confirmed that the proposed approach has potential in identifying the tumor type.

Discrimination of quarry blasts and microearthquakes using adaptive neuro-fuzzy inference systems in the Tehran region

The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Iranian National Network Center (BIN). In the south and southeast of Tehran, a large number of quarry blasts contaminate the earthquake catalog. In order to identify the real seismicity (tectonic earthquakes) in the region, we need to discriminate quarry blasts from natural earthquakes in the catalog. ANFIS classifiers were used for identifying and categorizing the two kinds of seismic events. Neuro-fuzzy coding was applied using six features as ANFIS inputs. We investigated waveforms of 506 seismic events (0.9<ML<4.2) from the BIN network and determined differences between earthquakes and quarry blasts based on input features. These features include the original time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and two features for spectral analysis of their seismograms. The results of this study indicate that, out of a total of 506 seismic records, the neuro-fuzzy ANFIS approach identified 24.5% (124 events) and 75.5% (386 events) were quarry blasts and natural earthquakes, respectively. Our revised earthquake catalog provides improved data for realistic earthquake hazard assessment. Moreover, active faults will be detected correctly.

An Efficient Automatic Overlapped Fingerprint Identification and Recognition Using ANFIS Classifier

The Automatic Fingerprint Recognition System plays an important role in forensics and law enforcement applications. The objective of the proposed system in the current study is to identify and separate overlapped fingerprint images automatically using an Adaptive Neuro Fuzzy Inference System (ANFIS) Classifier. There are various issues that have been identified, which need to be addressed to develop the scope of light–out fingerprint recognition system. The latent fingerprint images can be overlapped in crime scenes. During investigations, there are several possibilities for acquiring damaged or overlapped fingerprint images. The proposed system analyzes and identifies the overlapped images using an ANFIS Classifier. This paper also proposes a novel algorithm for the separation of overlapped images. The proposed work is designed to retrieve fast and accurate data using fingerprint identification for the overlapped images. Extensive experiments are performed on the FVC 2006 DB1-A, DB2-A, NIST SD27 and SLF databases. The experimental results are highly promising and outperform the previous systems in identifying the overlapped images. Our proposed system separates those overlapped fingerprints more accurately and robustly. The achieved results confirmed that the proposed automatic fingerprint recognition system has higher possibility of overlapped fingerprint detection.

An Efficient Feature Extraction and Dyslexia Detection from Eye Movements Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

The major objective of the work is concern about the education community in their mission to be with the majority. The initial stage of the work is to study different mechanism for diagnosing learning disability (LD). Medical diagnosing searching becomes the major problem for movements of relationship among eye and dyslexia. To manage these problems several work have been demonstrated. But still it becomes most important difficulty how to extraction of best features from original data and how to improves the detection result in learning disability (LD). In order overcome these difficulties proposed an efficient wavelet transform (WT) for feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) based classifier dyslexia detection from eye movement signal. Eye movement of children was evaluated using Videooculographic (VOG) techniques. During this process both reading and non-reading tasks were measured based on eye movement of each children. After the measurement of eye movement then extract features using WT, then further the diagnosis of learning disability for classification using ANFIS system. Finally evaluate the performance of ANFIS classifier for dyslexia detection from eye movement signal. Compare to ANN based classification ,proposed ANFIS Classifier are less computational complication and detection result of methods are compared using parameters like accuracy, sensitivity and specificity, it shows that ANFIS Classifier are higher detection result than existing ANN classification

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Automatic Detection of Hard Exudates from Fundus Images Using Adaptive Neuro-Fuzzy Inference System

Diabetic retinopathy is one of the most significant factors contributing to blindness, and so diagnosis and timely treatment is particularly important to prevent visual loss. Exudates are a primary sign of diabetic retinopathy and early detection could limit the disease’s severity. Hard exudates are detected using a combination of colour histograms in the HSV (hue, saturation, value) colour space, and the split and merge technique with the predicate function involving contrast measures to calculate edge pixels. Intensity, textural features using second-order statistics are extracted from fundus images and given as input to an adaptive neuro-fuzzy inference system (ANFIS) to detect the exudates pixels and non-exudates pixels. The proposed method is evaluated on 415 real-time images comprising 200 normal and 215 non-proliferative eyes, and these are compared with the performance of human graders. Performance of the classifier is measured by the receiver operating characteristic curve. ANFIS classifier provides classification with 99.1% accuracy and less convergence time when compared with the performance of back-propagation network. The method can also be used as a screening tool in the analysis and diagnosis of retinal images.

Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systems

Abstract: A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.

Classification of Cardiac Arrhythmia Based on Hybrid System

This paper, describes an Intelligent Diagnosis System using Hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals, and comparison this Technique with Feed-Forward Neural Network (FFNN), and Fuzzy Inference Systems (FIS). Feature extraction using Independent Component Analysis (ICA) and power spectrum, together with the RR interval then serve as input feature vector, this feature were used as input of FFNN, FIS, and ANFIS classifiers. six types of ECG signals they are Normal Sinus Rhythm (NSR), Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC), Ventricular Tachycardia(VT), Ventricular Fibrillation (VF) and Supraventricular Tachycardia (SVT). The results indicate a high level of efficient, the proposed method outperforms the other methods with an impressive accuracy of 97.1%, As for other methods FFNN, FIS results were respectively 94.3%, 95.7%.

Recognition of Fusarium diseased corn kernels using MIR spectroscopy and Neuro – Fuzzy classifier

In this paper we present the results obtained recently by recognizing corn kernels (healthy and Fusarium diseased) using spectral characteristics and Neuro-Fuzzy classifier. The proposed approach to corn kernel type diagnostic is based on three steps: spectral data acquisition, preliminarily processing and feature extraction, and classification. In the first step, the near infrared reflectance spectral characteristics are obtained in the range 400 – 2500 nm with step 2 nm. In the second step informative features are extracted using principal component analysis. In order to make a compact spectral data representation, the number of features is reduced. Adaptive Neuro – Fuzzy Inference System (ANFIS) is implemented in order to establish decision boundaries in the space of the selected features which separate samples belonging to two different classes (healthy and Fusarium diseased corn kernels). The inputs to ANFIS are several principal components. ANFIS has only one output node, the corn kernel type. The system is trained by data of train sample. We then evaluate the deviation of our estimate compared to the expert assessment. The procedure is applied to the 1800 corn kernels spectral data and the average accuracy is given. The average testing error for class healthy is 0,023 % and for class diseased is 0,329 %. As a result of the presented experiments the NIR spectroscopy is applicable as a nondestructive tool for Fusarium diseased corn kernels recognition and may become a powerful tool for monitoring the safety of our food supply. For practical implementation in the grain industry, a ANFIS classifier can be used effectively.

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means “cessation of breath” during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity due to hypopnoea episodes.

Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals.

Comparison of Different Classifier Algorithms on the Automated Detection of Obstructive Sleep Apnea Syndrome

In this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apneahypopnea index (AHI), SaO2 minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO2 intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI>5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.

An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases

In the last two decades, the use of artificial intelligence methods in biomedical analysis is increasing. This is mainly because of the effectiveness of classification and detection systems have improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the use of linear discriminant analysis (LDA) and adaptive neuro-fuzzy inference system (ANFIS) to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white-denoising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short-time Fourier transform were used. As next step, wavelet entropy was applied to these features. For reducing the complexity of the system, LDA was used for feature reduction. In the classification stage, ANFIS classifier is chosen. To evaluate the performance of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 94% specificity rate was obtained.

Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification

The wavelet domain features have been intensively used for texture classification and texture segmentation with encouraging results. More of the proposed multi resolution texture analysis methods are quite successful, but all the applications of the texture analysis so far are limited to gray scale images. This paper investigates the usage of Wavelet transform (WT) and Adaptive neuro-fuzzy inference system (ANFIS) for color texture classification problem. The proposed scheme composed of a wavelet domain feature extractor and an ANFIS classifier. Both entropy and energy features are used on wavelet domain. Different color spaces are considered in the experimental studies. The performed experimental studies show the effectiveness of the wavelet transform and ANFIS structure for color texture classification problem. The overall success rate is over 96%.

An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose (sugar). Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. In this paper, we have detected on diabetes disease, which is a very common and important disease using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. The proposed system has two stages. In the first stage, dimension of diabetes disease dataset that has 8 features is reduced to 4 features using principal component analysis. In the second stage, diagnosis of diabetes disease is conducted via adaptive neuro-fuzzy inference system classifier. We took the diabetes disease dataset used in our study from the UCI (from Department of Information and Computer Science, University of California) Machine Learning Database. The obtained classification accuracy of our system was 89.47% and it was very promising with regard to the other classification applications in literature for this problem.

Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems

A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. The six ANFIS classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classifier (combining ANFIS) was trained using the outputs of the six ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.