Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution

2012 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
R. R. Janghel ◽  
Ritu Tiwari ◽  
Rahul Kala ◽  
Anupam Shukla
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Cancer Disease ◽  
Data Set ◽  
Computing Technique ◽  
Cancer Data ◽  
Evolutionary Neural Network ◽  
Hidden Layer

In this paper a new approach for the prediction of breast cancer has been made by reducing the features of the data set using PCA (principal component analysis) technique and prediction results by simulating different models namely SANE (Symbiotic, Adaptive Neuro-evolution), Modular neural network, Fixed architecture evolutionary neural network (F-ENN), and Variable Architecture evolutionary neural network (V-ENN). The dimensionality reduction of the inputs achieved by PCA technique to an extent of 33% and further different models of the soft computing technique simulated and tested based on efficiency to find the optimum model. The SANE model includes maximum number of connections per neuron as 24, evolutionary population size of 1000, maximum neurons in hidden layer as 12, SANE elite value of 200, mutation rate of 0.2, and number of generations as 100. The simulated results reflect that this is the best model for the prediction of the breast cancer disease among the other models considered in the experiment and it can effectively assist the doctors for taking the diagnosis results as its efficiency found to be 98.52% accuracy which is highest.

scholarly journals Learning algorithm analysis for deep neural network with ReLu activation functions

2018 ◽  
Vol 19 ◽  
pp. 01009
Author(s):  
Stanisław Płaczek ◽  
Aleksander Płaczek
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Activation Function ◽  
Point Of View ◽  
Algorithm Analysis ◽  
Theoretic Point ◽  
Neural Network Structure ◽  
Null Value ◽  
Hidden Layer

In the article, emphasis is put on the modern artificial neural network structure, which in the literature is known as a deep neural network. Network includes more than one hidden layer and comprises many standard modules with ReLu nonlinear activation function. A learning algorithm includes two standard steps, forward and backward, and its effectiveness depends on the way the learning error is transported back through all the layers to the first layer. Taking into account all the dimensionalities of matrixes and the nonlinear characteristics of ReLu activation function, the problem is very difficult from a theoretic point of view. To implement simple assumptions in the analysis, formal formulas are used to describe relations between the structure of every layer and the internal input vector. In practice tasks, neural networks’ internal layer matrixes with ReLu activations function, include a lot of null value of weight coefficients. This phenomenon has a negatives impact on the effectiveness of the learning algorithm convergences. A theoretical analysis could help to build more effective algorithms.

scholarly journals Constructive Deep Neural Network for Breast Cancer Diagnosis

2018 ◽  
Vol 51 (27) ◽  
pp. 98-103 ◽  
Author(s):  
R. Zemouri ◽  
N. Omri ◽  
B. Morello ◽  
C. Devalland ◽  
L. Arnould ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis

scholarly journals Polynomial Fitting Algorithm Based on Neural Network

2021 ◽  
Vol 1 (1) ◽  
pp. 32-39
Author(s):  
Yuerong Tong ◽  
Lina Yu ◽  
Sheng Li ◽  
Jingyi Liu ◽  
Hong Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Linear Functions ◽  
Polynomial Fitting ◽  
Main Research ◽  
Fitting Algorithm ◽  
Input Variables ◽  
Hidden Layer ◽  
Implicit Polynomial

As a method of function approximation, polynomial fitting has always been the main research hotspot in mathematical modeling. In many disciplines such as computer, physics, biology, neural networks have been widely used, and most of the applications have been transformed into fitting problems using neural networks. One of the main reasons that neural networks can be widely used is that it has a certain sense of universal approximation. In order to fit the polynomial, this paper constructs a three-layer feedforward neural network, uses Taylor series as the activation function, and determines the number of hidden layer neurons according to the order of the polynomial and the dimensions of the input variables. For explicit polynomial fitting, this paper uses non-linear functions as the objective function, and compares the fitting effects under different orders of polynomials. For the fitting of implicit polynomial curves, the current popular polynomial fitting algorithms are compared and analyzed. Experiments have proved that the algorithm used in this paper is suitable for both explicit polynomial fitting and implicit polynomial fitting. The algorithm is relatively simple, practical, easy to calculate, and can efficiently achieve the fitting goal. At the same time, the computational complexity is relatively low, which has certain application value.

scholarly journals PIXEL-BASED AND OBJECT-BASED TERRACE EXTRACTION USING FEED-FORWARD DEEP NEURAL NETWORK

2019 ◽  
Vol IV-3/W1 ◽  
pp. 1-7
Author(s):  
H. T. Do ◽  
V. Raghavan ◽  
G. Yonezawa
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Reference Data ◽  
Back Propagation ◽  
Activation Function ◽  
Rate Of Change ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Object Based ◽  
Hidden Layer

<p><strong>Abstract.</strong> In this paper, we present the identification of terrace field by using Feed-forward back propagation deep neural network in pixel-based and several cases of object-based approaches. Terrace field of Lao Cai area in Vietnam is identified from 5-meter RapidEye image. The image includes 5 bands: red, green, blue, rededge and nir-infrared. Reference data are set of terrace points and nonterrace points, which are generated by randomly selected from reference map. The reference data is separated into three sets: training set for training processing, validation set for generating optimal parameters of deep neural network model, and test set for assessing the accuracy of classification. Six optimal thresholds (T): 0.06, 0.09, 0.12, 0.14, 0.2 and 0.22 are chosen from Rate of Change graph, and then used to generate six cases of object-based classification. Deep neural network (DNN) model is built with 8 hidden layers, input units are 5 bands of RapidEye, and output is terrace and non-terrace classes. Each hidden layer includes 256 units – a large number, to avoid under-fitting. Activation function is Rectifier. Dropout and two regularization parameters are applied to avoid overfitting. Seven terrace maps are generated. The classification results show that the DNN is able to identify terrace field effectively in both pixel-based and object-based approaches. Pixel-based classification is the most accurate approach, achieves 90% accuracy. The values of object-based approaches are 88.5%, 87.3%, 86.7%, 86.6%, 85% and 85.3% correspond to the segmentation thresholds.</p>

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