# Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM
## 2 Related Work  
Characteristic calcification is an important and most common manifestation in the early stage of breast cancer. The microcalcification point usually presents a small bright spot in the mammogram. It usually shows that several microcalcifications are clustered together, similar to the distribution of clusters. Each microcalcification has a good grayscale consistency, high brightness, few edge burrs, and high contrast with the surrounding background breast tissue, it is difficult to find microcalcification spots by the naked eye. Therefore, it is usually missed by doctors, which may lead to cancer misdiagnosis. Sometimes clinicians use a magnifying glass to check the mammogram. However, this method is not accurate and requires considerable time and energy. In recent years, researchers have used computers to begin diagnosis of breast calcification. There are three general classification methods for the detection of calcifications in mammography: (1) based on the image enhancement method, [PERSON] [12] proposed a quantum noise model by simply averaging the square root of the gray level to improve the local contrast and achieve microcalcification. [PERSON] et al. [13] employed commonly used enhancement techniques to compare detected results of microcalcification points, such as the contrast stretching, histogram modeling, convolution filters, region-based enhancement methods, and morphological methods.  
Based on the method of multi-scale decomposition (2), [PERSON] [14] proposed a method to extract high-frequency signals and noises using different images. Then, he used the wavelet reduction to suppress noises and employed the neural network classification to extract the distribution of microcalcification. [PERSON] et al. [15] defined a cost function by modifying the weight of wavelet coefficients by a conjugate gradient algorithm. They minimized the cost function and detected microcalcifications in second and third layers of high-frequency subbands, after the wavelet decomposition. (3) Based on the machine learning method, [PERSON] et al. [16] used the SVM algorithm to automatically detect microcalcifications. They showed that experimental results are superior to the commonly used artificial neural network classification methods. Moreover, [PERSON] et al. (2017) used supervised learning to detect the image segmentation. They used SVM to probe each region of the image to determine if it contains microcalcifications. [PERSON] (2018) proposed a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. They used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. [PERSON] (2019) explored a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operating characteristics curve of 0.785 (accuracy 71.19%) on the test set.