Figure 2.

ANN-SVM based image analysis pipeline. Image data sets selected for image pre-processing and segmentation (A) after quality evaluation. The image was enhanced by Gaussian filter. Template images were used to segment region of interest of upper and lower sections by 2D-cross correlation. Feature extraction (B) was performed on segmented images by multiple artificial neural networks (ANN). The extracted features were used for developing Support Vector Machine model (SVM) model for each ANN feature vectors (C). The receiver operating characteristic (ROC) curve analysis was performed in cross-validation data set to find out optimal cut-off score for classification. The SVM-model validated in test data set and classified according cut-off score. The ANN-SVM models were developed for stepwise classification- initially for classifying OSCC from dysplastic/normal/benign lesions and then dysplastic from benign/normal lesions. OSCC: Oral squamous cell carcinoma, HGD: High grade dysplasia–Moderate/Severe dysplasia, LGD: Low grade dysplasia- Mild dysplasia, hyperplasia, ANN: Artificial neural network, 2D- 2 dimensional, SVM: Support vector machine, ROC: Receiver Operating Characteristic.