Breast Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and 3TP U-Net Deep Convolutional Neural Network

Authors

  • A. N. Srikanth M.Tech. Student, Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India
  • H. S. Arvinda Associate Professor, Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India

Keywords:

Convolutional Neural Network (CNN), Artificial Neural Networks (ANN)

Abstract

Breast cancer affects one out of eight females worldwide. It is diagnosed by detecting the malignancy of the cells of breast tissue. Modern medical image processing techniques work on histopathology images captured by a microscope, and then analyze them by using different algorithms and methods. Machine learning algorithms are now being used for processing medical imagery and pathological tools. Manual detection of a cancer cell is a tiresome task and involves human error, and hence computer-aided mechanisms are applied to obtain better results as compared with manual pathological detection systems. In deep learning, this is generally done by extracting features through a convolutional neural network (CNN) and then classifying using a fully connected network. Deep learning is extensively utilized in the medical imaging field, as it does not require prior expertise in a related field. In this paper, we have trained a convolutional neural network and obtained a prediction accuracy of up to 99.86%.

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Published

13-07-2020

How to Cite

[1]
A. N. . Srikanth and H. S. . Arvinda, “Breast Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and 3TP U-Net Deep Convolutional Neural Network”, IJRESM, vol. 3, no. 7, pp. 23–25, Jul. 2020.

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Section

Articles