Artificial Neural Network for Classifying Injected Materials under Ultrasonography

Galuh Retno Utari; Giner Maslebu; Suryasatriya Trihandaru

doi:10.26877/asset.v3i1.8324

Artificial Neural Network for Classifying Injected Materials under Ultrasonography

Galuh Retno Utari, Giner Maslebu, Suryasatriya Trihandaru

Abstract

We have constructed an artificial neural network (ANN) architecture to classify four different classes of ultrasonography recorded from a jelly box phantom that was injected by iron, glass, or plastic marble, or without any injection. This jelly box was made as a phantom of a human body, and the injected materials were the cancers. The small size of the injected materials caused only little disturbances those could not easily distinguished by human eyes. Therefore, ANN was used for classifying the different kind of the injected materials. The number of original imagestaken from ultrasonographs were not so many, therefore we did data augmentation for providing large enough dataset that fed into ANN. The data augmentation was constructed by pixel shifting in horizontal and vertical directions. The procedure proposed here produced 98.2% accuracy for predicting test dataset, though the result was sensitive to the choice of augmentation area.

Keywords

Artificial Neural Network; Tensorflow; Multiclass Classification

Full Text:

PDF

References

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, 27-May-2015,https://doi.org/10.1038/nature14539

S. Moein, “Artificial Neural Network for Medical Diagnosis,” in Medical Diagnosis Using Artificial Neural Networks, IGI Global, 2014, pp. 85–94.ebook

H. J. Lee et al., “Role of transrectal ultrasonography in the prediction of prostate cancer: artificial neural network analysis.,” J. Ultrasound Med., vol. 25, no. 7, pp. 815-21–4, Jul. 2006, https://doi.org/10.7863/jum.2006.25.7.815

W. Wu et al., “An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.,” Comput. Math. Methods Med., pp. 1–10, Jul. 2020. https://doi.org/10.1155/2020/6789306

C. Zhang, J. Zhao, J. Niu, and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms.,” PLoS One, vol. 15, no. 8, pp. 1–20, Aug. 2020. https://doi.org/10.1371/journal.pone.0237674

M. Pandey et al., “Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing.,” PLoS One, vol. 15, no. 7, pp. 1–15, Jul. 2020. https://doi.org/10.1371/journal.pone.0236827

J. Zhou, J. He, G. Li, and Y. Liu, “Identifying Capsule Defect Based on an Improved Convolutional Neural Network.,” Shock Vib., pp. 1–9, Jul. 2020. https://doi.org/10.1155/2020/8887723

K. Almezhghwi and S. Serte, “Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network.,” Comput. Intell. Neurosci., pp. 1–12, Jul. 2020. https://doi.org/10.1155/2020/6490479

R. Shanmugamani, Deep Learning for Computer Vision : Expert Techniques to Train Advanced Neural Networks Using TensorFlow and Keras. Birmingham, UK: Packt Publishing, 2018. ebook

M. Dong, S. Mu, A. Shi, W. Mu, and W. Sun, “Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network,” Int. J. Agric. Biol. Eng., vol. 13, no. 4, pp. 205–210, 2020, https://doi.org/10.25165/j.ijabe.20201304.4826

Ö. F. Ertuğrul, “A novel type of activation function in artificial neural networks: Trained activation function,” Neural Networks, vol. 99, pp. 148–157, Mar. 2018, https://doi.org/10.1016/j.neunet.2018.01.007

K. Fukushima, “Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements,” IEEE Trans. Syst. Sci. Cybern., vol. 5, no. 4, pp. 322–333, 1969, https://doi.org/10.1109/TSSC.1969.300225

B. Gao and L. Pavel, “On the properties of the softmax function with application in game theory and reinforcement learning,” arXiv. arXiv, 03-Apr-2017. https://arxiv.org/abs/1704.00805

L. Li, M. Doroslovacki, and M. H. Loew, “Approximating the Gradient of Cross-Entropy Loss Function,” IEEE Access, vol. 8, pp. 111626–111635, 2020, https://doi.org/10.1109/ACCESS.2020.3001531

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., Dec. 2014. https://arxiv.org/abs/1412.6980

DOI: https://doi.org/10.26877/asset.v3i1.8324

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Advance Sustainable Science, Engineering and Technology (ASSET)

E-ISSN: 2715-4211
Published by Science and Technology Research Centre

Universitas PGRI Semarang, Indonesia

Website: http://journal.upgris.ac.id/index.php/asset/index
Email: [email protected]