scholarly journals Classification of Breast Lesions using Modified Masood Score and Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1745-1748
Author(s):  
Sonali Nandish Manoli ◽  
Anand Raj Ulle ◽  
N.M. Nandini ◽  
T.S. Rekha
Keyword(s):  
Neural Network ◽  
Breast Lesion ◽  
Breast Lesions ◽  
Observer Variability ◽  
Feed Forward Neural Network ◽  
Inter Observer Variability ◽  
Class 1 ◽  
Novel Method ◽  
Nuclear Features

In this paper, we propose a novel method to classify Breast Lesions based on minute changes in the cell and nuclear features of the cell. It is important to note these changes as they play a significant role in diagnosis and the line of treatment by an oncologist. To overcome the problem of inter-observer variability the method of scoring is used to grade the lesions considered for the study. We have used the Modified Masood Score and designed an algorithm which classifies a given breast lesion into 6 classes namely Benign, Intermediate class-1,Intermediate class-2, Malignant class-1,Malignant class-2 and Malignant class-3. We have developed a sensitive model using the feed-forward neural network and Pattern Network to achieve the above objective. The Rank of the features is observed using ReliefF Algorithm.

Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network

2014 ◽  
Vol 119 ◽  
pp. 89-97 ◽  
Author(s):  
Maciej Oczak ◽  
Stefano Viazzi ◽  
Gunel Ismayilova ◽  
Lilia T. Sonoda ◽  
Nancy Roulston ◽  
...  
Keyword(s):  
Neural Network ◽  
Aggressive Behaviour ◽  
Activity Index ◽  
Feed Forward Neural Network ◽  
Feed Forward

scholarly journals Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ravi Kiran ◽  
Dayakar L. Naik
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Analytical Quality ◽  
Feed Forward Neural Network ◽  
First Derivative ◽  
Network Output ◽  
Novel Method ◽  
Classification Tasks ◽  
Trained Neural Network ◽  
Sensitivity Method

AbstractEvaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.

A feed forward neural network for classification of bull's-eye myocardial perfusion images

1995 ◽  
Vol 22 (2) ◽  
pp. 108-115 ◽  
Author(s):  
David Hamilton ◽  
Peter J. Riley ◽  
Ueber J. Miola ◽  
Ahmed A. Amro
Keyword(s):  
Neural Network ◽  
Myocardial Perfusion ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Bull's Eye

scholarly journals A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6491
Author(s):  
Le Zhang ◽  
Jeyan Thiyagalingam ◽  
Anke Xue ◽  
Shuwen Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Signal Amplitude ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Amplitude Change ◽  
Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

Sign in / Sign up

Export Citation Format

Share Document