COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY

1993 ◽  
Vol 07 (06) ◽  
pp. 1417-1436 ◽  
Author(s):  
KEVIN S. WOODS ◽  
CHRISTOPHER C. DOSS ◽  
KEVIN W. BOWYER ◽  
JEFFREY L. SOLKA ◽  
CAREY E. PRIEBE ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Computer Assisted ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Dynamic Neural Networks ◽  
Pattern Recognition Techniques ◽  
Mammographic Images ◽  
Bright Spots ◽  
Computer Assisted Detection

Computer-assisted detection of microcalcifications in mammographic images will likely require a multistage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper focuses on the first three of these stages, and especially on the classification of segmented local bright spots as either calcification or noncalcification. Seven classifiers (linear and quadratic classifiers, binary decision trees, a standard backpropagation network, 2 dynamic neural networks, and a K-nearest neighbor) are compared. In addition, a postprocessing step is performed on objects identified as calcifications by the classifiers to determine if any clusters of microcalcifications exist. A database of digitized film mammograms is used for training and testing. Detection accuracy of individual and clustered microcalcifications is compared across the seven methods using area under the ROC curve as a figure of merit.

ICA as Pattern Recognition Technique for Gesture Identification

2013 ◽  
pp. 530-549
Author(s):  
Ganesh Naik ◽  
Dinesh Kant Kumar ◽  
Sridhar Arjunan
Keyword(s):  
Pattern Recognition ◽  
Marked Improvement ◽  
Mitigation Strategy ◽  
The Other ◽  
Computer Assisted ◽  
Pattern Recognition Techniques ◽  
Non Invasive ◽  
Robust System ◽  
Network Weight ◽  
Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

Improved Indoor Geo-Localization System

2019 ◽  
pp. 183-206
Author(s):  
Abdaoui Noura ◽  
Ismahène Hadj Khalifa ◽  
Sami Faiz
Keyword(s):  
Sensor Networks ◽  
Recent Progress ◽  
Nearest Neighbor ◽  
Low Energy ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Smart Object ◽  
Low Energy Consumption ◽  
Localization Systems

In the concept of internet of things (IOT), physical position of smart object is very useful for relevant function over sensor networks. However, the invalid information of indoor geo-localization systems relative to these wireless sensor compromises the intelligence of IOT network. Therefore, this chapter produces the recent progress in the indoor geo-localization systems and the IOTs area. It defines the best indoor geo-localization technologies that meet their needs while respecting the constraints related to sensor networks. This framework combines between simplicity of Bluetooth low energy (BLE), popular wi-fi infrastructure, and the k-nearest neighbor (KNN) algorithm (in order to filter the initial fingerprint dataset). This new conception increases real-time detection accuracy and guarantees the low energy consumption.

ICA as Pattern Recognition Technique for Gesture Identification

2012 ◽  
pp. 367-387
Author(s):  
Ganesh Naik ◽  
Dinesh Kant Kumar ◽  
Sridhar Arjunan
Keyword(s):  
Pattern Recognition ◽  
Marked Improvement ◽  
Mitigation Strategy ◽  
The Other ◽  
Computer Assisted ◽  
Pattern Recognition Techniques ◽  
Non Invasive ◽  
Robust System ◽  
Network Weight ◽  
Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

scholarly journals Automatic detection of pharyngeal fricative in cleft palate speech

2018 ◽  
Vol 246 ◽  
pp. 03007
Author(s):  
Fei He ◽  
Geyi Zhou ◽  
Xinyi He ◽  
Heng Yin ◽  
Ling He
Keyword(s):  
Cleft Palate ◽  
Nearest Neighbor ◽  
Automatic Detection ◽  
Acoustic Parameter ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Acoustic Feature ◽  
Nearest Neighbor Classifier ◽  
Cleft Palate Speech ◽  
Neighbor Classifier

Pharyngeal fricative occurs during the production of consonants, which makes the consonants lose or weaken in cleft palate speech. In clinical application, the automatic detection of pharyngeal fricative in cleft palate speech could provide objective and effective assistant aids for speech language pathologists. In this paper, a novel acoustic parameter is proposed to detect the existence of pharyngeal fricative in cleft palate speech. This proposed acoustic feature ICPD (Independent Consonant Prominent Distribution) reflects the movement of mouth and tongue. The experimental results show that normal fricative has the higher ICPD. The extracted ICPD feature is combined with k-nearest neighbor classifier to achieve the automatic detection of pharyngeal fricative. The proposed system is tested on 127 speech samples recorded by cleft palate patients and 94 by normal speakers of controls. The overall pharyngeal fricative detection accuracy is around 90%.

Myocardial Tissue Characterization Using Pattern Recognition Procedures on Backscattered Ultrasonic Signals

1986 ◽  
Vol 8 (3) ◽  
pp. 181-195
Author(s):  
R.A.G. Dyer ◽  
S.A. Dyer ◽  
P.K. Bhagat
Keyword(s):  
Pattern Recognition ◽  
Minimum Distance ◽  
Tissue Characterization ◽  
Nearest Neighbor ◽  
Myocardial Tissue ◽  
Ultrasonic Signals ◽  
Pattern Recognition Techniques ◽  
Myocardial Tissue Characterization ◽  
Backscattered Signals

Pattern recognition techniques were applied to backscattered signals obtained in vitro from normal and abnormal canine and human heart samples. Orthogonal transforms, in conjunction with the variance criterion, comprised the feature extractors. The minimum-distance (MD) and nearest-neighbor (NN) rules were used as classifiers. When the MD rule was used, the magnitude of the DFT gave the best performance for both canine and human samples. When the NN rule was used, all the transforms performed comparably. The classification performances were improved for both species when the NN rule was used with feature extractors containing phase information.

scholarly journals Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

2018 ◽  
Vol 8 (1) ◽  
pp. 483
Author(s):  
Khairul Anam ◽  
Adel Al-Jumaily
Keyword(s):  
Pattern Recognition ◽  
Extreme Learning Machine ◽  
Healthy Subjects ◽  
Nearest Neighbor ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

scholarly journals Pear Defect Detection Method Based on ResNet and DCGAN

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 397
Author(s):  
Yan Zhang ◽  
Shiyun Wa ◽  
Pengshuo Sun ◽  
Yaojun Wang
Keyword(s):  
Defect Detection ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Detection System ◽  
Implementation Process ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Generation Network ◽  
Low Efficiency

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.

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