The Investigation of Deep Convolutional Neural Network for Diagnosing Breast Cancer in Thermographic Images

Document Type : Original Article

Authors

1 Assistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan.

2 Assistant Professor of Biomedical Engineering , Department of Electrical Engineering , Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3 Assistant Professor of control Engineering, Dept. of Electrical Engineering,Faculty of Engineering, Vali-E-Asr University of Rafsanjan, Rafsanjan, Iran.

4 Associate Professor of Radiology Department of Radiology, School of Medicine Advanced Diagnostic and Interventional Radiology Research Center, Tehran, Iran.

Abstract

Abstract
Background and Objectives: Computer-aided design diagnostic systems are widely used in the differential diagnosis of breast cancer. Therefore, improving the accuracy of a CAD system has become an important field of research. In this paper, we investigated CAD systems based on deep neural networks of convolution type to detect breast cancer in thermographic images.
Materials and Methods: For analyzing the proposed model, the DMR database has been used. The number of the participants examined were 196, including 41 cases of cancer and 155 healthy subjects. Each person had 10 images of thermography. The total number of the analyzed images included 1960 images of thermography. The classification of thermal images including cancerous and healthy images is based on three types of deep convolution neural networks including google net, resnet18 and vgg16.
Results: The accuracy and specificity of the results using a neural network models of deep pre-training on google-net, resnet18 and vgg16 is 85.03%-89.7%, 83.8% -91.9% and 85.03% -91,01% respectively. The proposed model is capable of providing a significant response to the different breast tissue morphologies.
Conclusion: The model of deep artificial neural network can be used as an efficient and intelligent way to detect cancer in original thermal images without extracting features. However, more studies are needed to design other models of artificial neural networks based on deep learning to detect malignant or benign cancer in thermal imagery.

Keywords

References

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Jundishapur Scientific Medical Journal
Volume 18, Issue 6 - Serial Number 123
March and April 2020
Pages 615-629

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History

  • Receive Date: 28 March 2019
  • Revise Date: 30 December 2019
  • Accept Date: 19 February 2020

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