AUTOMATED DIAGNOSIS OF BRAIN TUMOURS ASTROCYTOMAS USING PROBABILISTIC NEURAL NETWORK CLUSTERING AND SUPPORT VECTOR MACHINES

2005 ◽  
Vol 15 (01n02) ◽  
pp. 1-11 ◽  
Author(s):  
DIMITRIS GLOTSOS ◽  
JUSSI TOHKA ◽  
PANAGIOTA RAVAZOULA ◽  
DIONISIS CAVOURAS ◽  
GEORGE NIKIFORIDIS
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Network Clustering ◽  
High Grade ◽  
Cell Nuclei ◽  
Decision Tree Classification ◽  
Average Accuracy ◽  
Malignancy Grading

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.

scholarly journals Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods

2019 ◽  
Vol 3 (4) ◽  
pp. 13-24 ◽  
Author(s):  
Naser Safdarian ◽  
Mohammadreza Hedyezadeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Screening Mammography ◽  
Fuzzy Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Final Decision ◽  
Pattern Recognition Methods

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other normal parts of the breast image. In this study, 19 final different features of each image were extracted to generate the feature vector for classifier input. The proposed method not only determined the boundary of masses but also classified the type of masses such as benign and malignant ones. The neural network classification methods such as the radial basis function (RBF), probabilistic neural network (PNN), and multi-layer perceptron (MLP) as well as the Takagi-Sugeno-Kang (TSK) fuzzy classification, the binary statistic classifier, and the k-nearest neighbors (KNN) clustering algorithm were used for the final decision of mass class. Results: The best results of the proposed method for accuracy, sensitivity, and specificity metrics were obtained 97%±4.36, 100%±0 and 96%±5.81, respectively for support vector machine (SVM) classifier. Conclusions: By comparing the results of the proposed method with the results of the other previous methods, the efficiency of the proposed algorithm was reported.

scholarly journals Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion

2020 ◽  
Vol 10 (18) ◽  
pp. 6417 ◽  
Author(s):  
Emanuele Lattanzi ◽  
Giacomo Castellucci ◽  
Valerio Freschi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Driver Behavior ◽  
Ground Truth ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Technology ◽  
Average Accuracy ◽  
Unsafe Behaviors ◽  
Vehicle Sensors

Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. In this work, we investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior. Starting from those signals, we computed a set of features capable to accurately describe the behavior of the driver. A support vector machine and an artificial neural network were then trained and tested using several features calculated over more than 200 km of travel. The ground truth used to evaluate classification performances was obtained by means of an objective methodology based on the relationship between speed, and lateral and longitudinal acceleration of the vehicle. The classification results showed an average accuracy of about 88% using the SVM classifier and of about 90% using the neural network demonstrating the potential capability of the proposed methodology to identify unsafe driver behaviors.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

scholarly journals Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

2020 ◽  
Author(s):  
V. Vijayasarveswari ◽  
A.M. Andrew ◽  
M. Jusoh ◽  
T. Sabapathy ◽  
R.A.A. Raof ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
The Other ◽  
Ultra Wideband ◽  
Support Vector ◽  
Data Normalization ◽  
Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

scholarly journals Fungal Disease in Cotton Leaf Detection and Classification using Neural Networks and Support Vector Machine

2019 ◽  
Vol 8 (10) ◽  
pp. 3664-3667 ◽  
Keyword(s):  
Support Vector Machine ◽  
Production Rate ◽  
Clustering Algorithm ◽  
Economic Status ◽  
Fungal Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Detection ◽  
Cotton Leaf ◽  
Average Accuracy

Agriculture productivity is the main factor for improving economic status of India. Reduction in production rate is mainly due to various diseases in plants. Identification of plant disease in early stage is the main challenge for improving the production rate as well as economic status. This paper presents automatic disease detection in cotton crop for three types of diseases Alternaria Leaf Spot Fungal Disease (ALSFD), Grey Mildew Cotton Disease (GMCD), and Rust Foliar Fungal Disease (RFFD). The K-means clustering algorithm is used for disease segmentation for cotton leaf. The diseased cluster is segmented into three clusters. From cluster 2 the features Mean , Contrast, Energy, Correlation, Standard Deviation, Variance , Entropy, and Kurtosis are extracted. The extracted features for 30 samples are given to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for disease classification. The performance of these classifiers are compared. The ALSF disease is classified 77.4% for ANN and 84.3% for SVM, GMC disease is 87.8% for ANN and 98.7% in SVM, RFF disease is 90.1%for ANN and 93.2% for SVM. The overall average accuracy of ANN classifier is 85.1% for three diseases and overall average accuracy for SVM is 92.06% for three diseases. It is clearly observed from the analysis SVM classifier gives accurate disease detection compared to ANN.

scholarly journals Computational method for predicting Self-Interactions Protein using Recurrent Neural Network from Protein Evolutionary Information

2020 ◽  
Author(s):  
Ji-Yong An
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Biological Activities ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Scale Invariant ◽  
Average Accuracy

Abstract Self-interactions Protein (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the paper, we presented a novelty computational method called RRN-SIFT, which combines the Recurrent Neural Network (RNN) with Scale Invariant Feature Transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it used SIFT for extracting key feature by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM) and employed RNN classifier to carry out classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on yeast and human dataset. We also compared our performance with the Back Propagation Neural Network (BPNN), the state-of-the-art support vector machine (SVM) and other exiting methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than those of the BPNN, SVM and other previous methods in the domain. Therefore, we can come to the conclusion that the proposed RNN-SIFT model is useful tools and can execute incredibly well for predicting SIPs, as well as other bioinformatics tasks. In order to facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed, and is available at http://219.219.62.123:8888/RNNSIFT/ and includes source code and SIPs datasets.

scholarly journals Simulation And Auditing Of Network Security Based On Probabilistic Neural Network Approach

2012 ◽  
Vol 3 (1) ◽  
pp. 91-94
Author(s):  
Manoj Patel ◽  
Dr. Maneesh Shrivastava ◽  
Kavita Deshmukh
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Multiple Characteristics ◽  
Simulation Purpose

: Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we use the probabilistic neural network approach that gives more efficient and accurate results as comparison to the clustering algorithm in the previous systems was used. The performance of PNN (probabilistic neural network) approach is improved  for identifying the particular attack like as wormholes, black holes  and selfish

scholarly journals Penentuan Notasi Gamelan Rindik Menggunakan Metode Transformasi Wavelet

2018 ◽  
Vol 17 (3) ◽  
pp. 319
Author(s):  
I Gusti Made Meri Utama Yasa ◽  
Linawati Linawati ◽  
N Paramaita
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Wavelet Function ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Data Process ◽  
Average Accuracy ◽  
Accuracy Level

Abstract—This paper present about recognition of gamelan rindik pattern using wavelet transform. Wavelet transform is used to find the special characteristic of gamelan rindik, which had previously been recorded and stored in computer with format *.wav. The data was subsequently used as training and tested data, Probabilistic Neural Network (PNN) was used to recognize gamelan rindik pattern using. The training and tasted data process used four different rindics, consisting 0f 240 gamelan rindik data. Discrete Wavelet Transform (DWT) was used as the method of feature extraction, with Symlet, Haar, and Daubechies Wavelet function. Those three functions of the wavelet  shows the average accuracy level for Symlet 94.58%, Haar 93.33%, and wavelet Daubechies 94.58%.

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