A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine

2021 ◽  
Vol 44 (1) ◽  
pp. 277-290
Author(s):  
Hossein Ketabi ◽  
Ali Ekhlasi ◽  
Hessam Ahmadi
Keyword(s):  
Support Vector Machine ◽  
Spectral Clustering ◽  
Automatic Detection ◽  
Support Vector ◽  
Digital Mammogram ◽  
Breast Masses ◽  
Computer Aided

Computer aided detection of breast masses in mammography using support vector machine classification

2011 ◽  
Author(s):  
Jan Lesniak ◽  
Rianne Hupse ◽  
Michiel Kallenberg ◽  
Maurice Samulski ◽  
Rémi Blanc ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Breast Masses ◽  
Support Vector Machine Classification ◽  
Computer Aided

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition

2019 ◽  
Vol 18 ◽  
pp. 153303381983074 ◽  
Author(s):  
Antonin Prochazka ◽  
Sumeet Gulati ◽  
Stepan Holinka ◽  
Daniel Smutek
Keyword(s):  
Support Vector Machine ◽  
Ultrasound Imaging ◽  
Random Forests ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Computer Aided ◽  
Binary Decomposition ◽  
Independent Features ◽  
Aided Diagnosis

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

scholarly journals Computer–aided diagnosis of diabetes using least square support vector machine

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Behnaz Naghash Almasi ◽  
Omid Naghash Almasi ◽  
Mina Kavousi ◽  
Amirhossein Sharifinia
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Computer Aided ◽  
Aided Diagnosis

A Winch Fault Classification Algorithm Based on Cluster Kernel Semi-Supervised Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 452-458
Author(s):  
Xian Xin Shi ◽  
Zhong Xiang Zhao ◽  
Chang Jian Zhu ◽  
Xiao Xiao Kong ◽  
Jun Fei Chai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Clustering ◽  
Cluster Algorithm ◽  
Original Data ◽  
Fault Classification ◽  
Support Vector ◽  
The Novel ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Cluster Assumption

A cluster kernel semi-supervised support vector machine (CKS3VM) based on spectral cluster algorithm is proposed and applied in winch fault classification in this paper. The spectral clustering method is used to re-represent original data samples in an eigenvector space so as to make the data samples in the same cluster gather together much better. Then, a cluster kernel function is constructed upon the eigenvector space. Finally, a cluster kernel S3VM is designed which can satisfy the cluster assumption of semi-supervised study. The experiments on winch fault classification show that the novel approach has high classification accuracy.

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