## 2 Research Status and Development Trends for Computer Aided Detection and Diagnosis of MRI
### Overview of Computer Aided Classification Algorithms for Cancer Diagnosis  
Combining computer-aided detection, classification and diagnosis of early breast cancer using MRI is quite interdisciplinary and requires a wide spectrum of expertise that can most often be found in well established laboratories, such as the University of California, Irvine (UCI) [26], the Korean National Seoul University [27] or the Hangzhou University of Electronic Science and Technology [18; 19]. There are also other well-known research institutes that have also made important advances by combining advances in detection, classification and diagnosis. In CADs, the most frequently used classification algorithms are rule-based classifiers, pattern matching networks, neural networks (NNs), Markov random field, support vector machines (SVMs) [28; 29; 30], linear discriminant classifiers (LDAs) [31], k-nearest neighbours (k-NN) [32], multi-layer artificial neural networks (ANNs), decision trees, etc. SVM is still the most commonly used classification method [33; 34]. In addition, ANNs are also powerful classification tools [35; 36]. Inspired by the artificial immune algorithm, [PERSON] et al. [37] used a semi-supervised algorithm to reduce classifier dependence on labelled data. Supervised information marked by professional doctors is introduced into the algorithm to guide the clustering process. By measuring the distance between the feature vectors of two samples, the probability that they are divided into the same category is calculated. Finally, the unlabelled samples are clustered with reference to membership. Using two well-known breast cancer benchmark datasets downloaded from the University of California, Irvine (UCI) machine learning database, the algorithm has shown significant potential for automatic classification and diagnosis of breast cancer. Because breast MRI examination involves very high resolution images of soft tissue, it can clearly distinguish the breast skin, subcutaneous fat gap, normal glands and diseased tissue. These high resolution images enable researchers to (i) distinguish between types of breast lesions and search for distinct features in the regions of these lesions, by identifying and analysing the background image features of each mammary gland through multiple dynamic parameter changes [38]; or (ii) to extract image features based on specific pixels in the segmentation [39; 40]; or (iii) to use a classifier for diagnosis and prediction of breast tumours according to the difference in the spatial morphology of the enhanced part of the lesion [41; 42]. There have been several approaches that show significant potential in improving MRI classifier performance. [PERSON] et al. [43] employed Markov random field and local binary pattern (LBP) to extract texture features of left and right breasts, respectively, then the authors applied the hidden Markov random field algorithms for classification at the decision level to achieve breast cancer diagnosis; [PERSON] et al. [44] adopted a regional growth algorithm and CNN to perform classification of benign and malignant breast tumours; [PERSON] et al. [45] selected 125 breast cancer patients divided into two groups. The observation group used a high field 1.5 Tesla magnetic resonance instrument to perform a higher contrast resolution DCE-MRI examination, and the control group underwent routine MRI examination, aiming at the comparison of the morphology and boundary feature detail between the two groups, taking also into consideration the size of the lesions in the two groups of patients. Minimal lesions were revealed, especially with the higher field instrument, and the types of breast tumours were determined and classified. [PERSON] et al. [PERSON] et al. [PERSON] et al. (2018) used six SVM algorithms to classify breast tumours in breast images obtained using a molybdenum target, conducted a detailed comparative analysis of their performance [PERSON] et al. [PERSON] et al.