Breast cancer diagnosis using multiple activation deep neural network
| Autor: | K. Vijayakumar, Vinod J. Kadam, Sudhir Kumar Sharma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
020205 medical informatics
Artificial neural network Computer science business.industry 020208 electrical & electronic engineering General Engineering Pattern recognition 02 engineering and technology medicine.disease Computer Science Applications Breast cancer Modeling and Simulation 0202 electrical engineering electronic engineering information engineering medicine Artificial intelligence business |
| Zdroj: | Concurrent Engineering. 29:275-284 |
| ISSN: | 1531-2003 1063-293X |
| DOI: | 10.1177/1063293x211025105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|