Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning

被引:16
作者
Cabioglu, Cagri [1 ]
Ogul, Hasan [2 ]
机构
[1] Baskent Univ, Dept Comp Engn, TR-06790 Ankara, Turkey
[2] Ostfold Univ Coll, Fac Comp Sci, N-1757 Halden, Norway
来源
BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2020) | 2020年 / 12108卷
关键词
Deep learning; Transfer learning; AlexNet; Thermal image; Image processing; Convolutional neural network; Breast cancer; THERMOGRAPHY;
D O I
10.1007/978-3-030-45385-5_64
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Networks (CNNs) have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that an architecture holding pre-trained convolutional layers and training newly added fully connected layers achieves a better performance compared with others. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.
引用
收藏
页码:716 / 726
页数:11
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