## 3 Proposed Approach
### Classification
#### 3.3.3 TCFA Detection Using Geometric Features  
Since TCFA detection involves subjective assessment [103], developing a comprehensive and automatic method for classification of plaque type using machine learning algorithms is essential. Inter-observer variability in the diagnosis of vulnerable plaque can be minimised by applying classification methods [54]. Geometric feature vectors extracted from all VH-IVUS images in the data set were horizontally concatenated to construct a feature matrix called FM of size 588 \(\times\) 18, where 588 indicates the number of VH-IVUS images and 18 the number of geometric features. Regarding the given criteria for TCFA, each row in the FM matrix is assigned a label of '1' or '0' to indicate the TCFA and non-TCFA, respectively. Therefore, 81 feature vectors were assigned as TCFA and 507 feature vectors were labelled as non-TCFA plaque. The assigned labels were attached at the end of each feature vector.  
FM is divided into two matrices, namely, FM\({}_{\text{Train}}\) (70%) and FM\({}_{\text{Test}}\) (30%). FM\({}_{\text{Train}}\) is used to build the model, while FM\({}_{\text{Test}}\) is applied to validate the model. In each validation step, FM\({}_{\text{Train}}\) is divided into five equal parts, consisting of four parts for the training data set and a fifth part for the testing data. This procedure is repeated five times, each time with a new part as test vector [91]. Each five-fold cross validation is run on FM\({}_{\text{Train}}\) ten times. Finally, the averages of sensitivity, specificity, and accuracy derived from 50 runs are reported as the total result of the classifier [104].  
In this section, different combinations of extracted features are examined to find the best technique for identifying TCFA plaque. Therefore, multiple classifiers, including the Back Propagation Neural Network (BPNN), kNN, and SVM, are examined to classify the plaque type into TFCA and non-TCFA. To achieve higher performance of SVM, FM\({}_{\text{Train}}\) is fed to the SVM classifier with linear, polynomial kernel functions in the order 1, 2, and 3, and Radial Basis Function (RBF) with different sigma values [69]. Different \(k\) values are applied to investigate the performance of k-NN classifier, as well [105]. The results in terms of accuracy (Acc), sensitivity (Sn), specificity (Sp), precision (Pr), and F-score (FS) are illustrated in Table 5.  
As illustrated in Table 5, the best result in terms of accuracy was obtained when SVM_Poly_3 and SVM_rbf_1.10 were applied, whereas the worst result was obtained for the SVM_MLP. On the other hand, SVM_rbf_1.10 and SVM_MLP demonstrated the best and the worst results, respectively, in terms of sensitivity. Furthermore, the best result in terms of specificity was obtained when SVM_Poly _2 and SVM_rbf_1.20 were used, whereas the worst result was obtained for the BPNN. Moreover, SVM_rbf_1.20 and BPNN showed the best and the worst results in terms of precision. Furthermore, the best results in terms of F-score were obtained when SVM_rbf_1.10 was applied, whereas the worst result was obtained for the SVM_MLP.  
Figure 12 illustrates the accuracy of multiple classifiers obtained based on geometric features. SVM_rbf_1.10 and SVM_Poly_3 achieved the best result for accuracy; therefore, they were selected to perform the TCFA detection using combined features.