Abstract
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.
Highlights
Breast cancer is the most common cancer among women
X-ray mammography is the most commonly used method for breast detection; it may cause damage owing to ionization, making it unsuitable for repeated mass screening measurements [2, 3]
The proposed network can be trained faster and requires fewer training steps to achieve the same accuracy compared with a simple convolutional neural networks (CNNs) without batch normalization
Summary
To reduce the mortality of breast cancer, early detection and an accurate diagnosis are important [1]. 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; 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; MRI incurs high costs, low specificities, and is not very convenient, which greatly limits its application [4,5,6].
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