## 2 Research Status and Development Trends for Computer Aided Detection and Diagnosis of MRI
### Overview of Computer Aided Classification Algorithms for Cancer Diagnosis
(2018) used a k-NN algorithm to diagnose breast cancer and the settings of the algorithm were further assessed in detail; [PERSON] et al. [PERSON] et al. (2018) applied the Naive Bayes algorithm for the classification of breast tumours. Furthermore, [PERSON] et al. [PERSON] et al. (2018) focused their attention on specific features of selected breast-occupying lesions which were also confirmed by pathological examination. Of the 46 lesions analyseanalysed, 31 (\(\approx\)67%) were malignant. The aim was to observe the distribution characteristics of the Signal-Time curve in relation to the MRI scan of the areas occupied by benign and malignant breast tissue and compare the specificity, sensitivity and accuracy of differential diagnosis of breast cancer performed with a MRI dynamic scan and a plain scan. On the basis of the intensity curves obtained at different time instances, lesions were categorised into three types: persistent enhancing type (Type 1), plateau type (Type 2) and washout type (Type 3). [PERSON] et al. [PERSON] et al. (2018) pathologic findings showed that the percentages of Type 1 benign and malignant lesions were 57 \(\pm\) 4% and 43 \(\pm\) 4%, the percentages of Type 2 lesions were 26 \(\pm\) 2% and 74 \(\pm\) 2% and the percentages of Type 3 lesions were 20 \(\pm\) 2% and 80 \(\pm\) 2%, respectively. An important conclusion of their work was that differences in the distribution of Signal-Time curve types among benign and malignant lesions, were consistently statistically significant. They also found that the sensitivity, specificity and accuracy for detecting breast malignant lesions using DCE-MRI was higher than that of an MRI plain scan. It was thus concluded that variations in the Signal-Time curve of DCE-MRI is an additional feature of clinical value and, therefore, it would be appropriate for it to be incorporated in any AI based classifier. Other works in the literature address problems related to DCE-MRI tumour classification by combining data interpolation (DCE-MRI), compressed sensing technology (DCE-MRI), tensor decomposition and reconstruction (DCE-MRI), deep learning (DCE-MRI), generative confrontation learning (DCE-MRI) and other methods. Further in-depth discussion of new methods for tumour classification using DCE-MRI can be found in (DCE-MRI). Although breast tissue DCE-MRI are highly sensitive, the specificity of the detection is very low. Identification of nonlinear and highly correlated variables hidden in high-dimensional data is a challenging task. Traditional MRI analysis is often limited to one-dimensional information, ignoring the correlation of other dimensions. DCE-MRI, however, is composed of a collection of high-dimensional image datasets. Drawbacks in the processing of multi-dimensional data sets include the high computational cost and sensitivity to noise. A primary requirement in the post-processing is thus to reduce the dimensionality of these datasets in order to save storage space. Secondly, due to the complexity and diversity of the data, dynamic high-dimensional images do not have linear characteristics, so linear transforms may not be used. The commonly used nonlinear dimensionality reduction techniques for dynamic image segmentation methods, are incompatible with singular value decomposition and principal component analysis. Furthermore, vectorial transformations are based on two-dimensional dynamic image sequences, and this approach does not take explicitly into account the fundamental difference between values associated with the time dimension and the space dimension. DCE-MRI provides sufficient spatial and temporal resolution for tumour image analysis at the same time. By adopting a spatio-temporal tensorial dataset framework, de-noising becomes more efficient because the variance in the noise across columns and rows in the tensor is co-averaged and tumour images with different structures can be transformed into simple geometric objects with greater precision.