- Original Article
- Open Access
Convolutional neural network for breast cancer diagnosis using diffuse optical tomography
© The Author(s) 2019
- Received: 9 October 2018
- Accepted: 11 April 2019
- Published: 8 May 2019
We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.
- Diffuse optical tomography
- Breast cancer
- Convolutional neural network
- Machine learning
- Image classification
Breast cancer is the most common cancer among women. To reduce the mortality of breast cancer, early detection and an accurate diagnosis are important . Thus, a promotable and sensitive detection technology with efficient statistical analysis methods are necessary for mass screening for breast cancer.
There are currently several clinical methods for breast cancer detection including X-ray mammography, magnetic resonance imaging (MRI), and ultrasound. X-ray mammography is the most commonly used method for breast detection; however, it may cause damage owing to ionization, making it unsuitable for repeated mass screening measurements [2, 3]. MRI can offer excellent images of breast tissue with higher sensitivities; however, MRI incurs high costs, low specificities, and is not very convenient, which greatly limits its application [4–6]. As a relatively inexpensive and nonionizing radiation method, ultrasound can differentiate between benign and malignant masses; however, it has a low sensitivity and is highly dependent on the skill of the technician [4, 7]. Other methods, such as photoacoustic imaging, are also emerging as diagnostic techniques for breast cancer detection [8–10]. Diffuse optical tomography (DOT) is an emerging method for breast tumor diagnosis that provides optical properties of breast tissue correlating to the tumor’s physiological signatures [11, 12]. As an imaging modality with endogenous contrast, DOT has the potential to overcome the limitations of the abovementioned modalities, particularly when screening dense breasts, in terms of safety, cost, portability, sensitivity, and specificity. DOT also allows for improved discrimination between malignant and benign lesions, making it a competitive mass screening method for breast cancer.
Thus far, several studies have applied computer-aided diagnosis (CAD) techniques to breast cancer detection; these techniques include the use of artificial neural networks [13–15], fuzzy logic [16, 17], Bayesian networks [18, 19], decision trees [20, 21], and k-means clustering [22, 23]. However, few researchers have implemented CAD methods with DOT to diagnose breast cancer. A study analyzed the absorption, scattering, and refractive index images obtained by a phase-contrast diffuse optical tomography system and demonstrated the sensitivity and specificity to be 0.82 and 0.92, respectively, with a support vector machine (SVM) classifier . Another study proposed a semi-automatic detection method of malignant breast lesions in DOT images using logistic regression of three optically measured physiological parameters; this method had an average sensitivity and specificity of 0.89 and 0.89, respectively .
Recently, convolutional neural networks (CNNs) have been proven to work well in the differentiation between benign and malignant breast lesions [26–30]. Compared with traditional methods, CNNs reduce the steps involved in image feature extraction; alternatively, they feed image data directly into the network that can learn discriminative features automatically. CNN architecture is particularly adapted to take advantage of the 2D structure of the input image.
In this work, a dataset of DOT breast images with 63 biopsy-confirmed tumor-bearing dense breasts were used. Dense breasts are common among young women and cannot be examined by existing mammography imaging systems . All images were collected from a DOT system and reconstructed with a finite element algorithm . In our previous work, an SVM classifier was used to classify breast tumors and achieved an accuracy of 71.7%. This study develops an optimized CNN adapted to the characteristics of diffusion tomography images; this proposed CNN shows a high accuracy on the breast cancer dataset.
2D diffuse optical tomography image dataset and its division into training and test sets
Each convolutional layer is followed by a batch normalization layer, which aims to stabilize the distributions of layer inputs by controlling the mean and variance . It adds two extra parameters per activation to preserve the representation ability of the network. Because of batch normalization, the network can tolerate increased training rates and often does not require dropouts for regularization. The normalized results then pass through a sigmoid nonlinear function as an output for these layers.
Pooling layers perform local averaging and subsampling, thereby reducing the resolution of the feature map and the sensitivity of the output to shifts and distortions. Pooling layers have a filter size of 2 × 2 pixels and output the average value offour inputs in each local region.
A simple logistic classifier is used in the final layer of the network. Malignant and benign are represented with 2-bit one-hot encoding as (1,0) and (0,1) in the output layer. The mini-batch stochastic gradient descent momentum is used for training. We also adopted an early stopping strategy to monitor the test misclassification rate (MCR), where training is terminated if no progress is noted on the test set. Optimum results were obtained by tuning the hyper parameters through an extensive set of trial and error experiments; finally, we used a learning rate of 0.3, mini-batch size of 83, and maximum epochs of 500.
Augmented dataset and its division into training and test sets
Comparison summary of the convolutional neural network performance on original and augmented data
Area under curve
The network is also trained in five groups of learning rates to obtain the best performance. The learning rate defines the speed at which the weight is updated on each epoch in the neural network. A network may learn fast but may be unstable and exhibit a very high learning rate. Conversely, a network that learns slowly may be easily susceptible local minimum trapping. Comparing the ROC curves in Fig. 3c, the suitable learning rate is determined to be 0.3.
During the study, we repeated the experiment several times and found that the test results show subtle differences. Thus, we trained and evaluated the CNN using a 10-fold cross-validation for robust testing. The ROC curve obtained is shown in Fig. 3d and the average AUC is found to be 0.93 (±0.03). This gives a sensitivity of 0.79 and a specificity of 0.97. It indicates that the CNN model is robust and is reliable for breast tumor classification. The average test accuracy rate reaches 91% with a standard deviation of 1.66%; this is a promising result achieved with little sample data.
Our results in this work show that it is possible to achieve a satisfactory result on breast cancer diagnosis using a CNN trained with DOT breast images. A DOT breast dataset is built; it includes 63 patient samples with malignant or benign tumors, for a total of 1260 2D gray scale images. Although it is an arduous task to detect a dense breast with a small set of data, the proposed 8-layer CNN has a good capability of classifying image patterns while achieving a specificity and sensitivity of 0.88 and 0.96, respectively. This technique has the potential to assist radiologists in diagnosing breast cancer and improving the diagnostic rate that furthermore promotes mass screening for breast cancer.
In future studies, more samples will be collected to improve generalization as the accuracy can be further improved with a larger dataset. It is important to carefully select a better convolutional network architecture and optimization algorithm. We would like to train a 3D CNN on 3D data, where spatial structures can provide information that may be missing or is far less obvious in 2D images. Unsupervised pre-training is also being considered for our diagnosis method in the future.
The images used in this study are reconstructed with optical absorption distribution of tissue; however, DOT not only provides the optical absorption coefficients but can also determine the optical scattering coefficients. As a functional optical imaging system, DOT can quantify the tumor hemoglobin concentration and blood oxygen saturation; these are directly related to tumor angiogenesis. The focus of our next research study will be how to make full use of this information.
This research was supported by the University of Electronic Science and Technology of China.
China Postdoctoral Science Foundation (No. 2018M633347).
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available due to personal privacy but are available from the corresponding author on reasonable request.
All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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