Figure 2: Plaque classification using deep learning.

A) Data preparation and preprocessing. Labelled images were manually extracted, categorised, and resized to a standard size (120×120). The images were converted to grayscale and adjusted for contrast. B) Model training and development. The datasets were split into training (n= 307) and test sets (n = 78). Training datasets were split further for training and validation to execute three-fold cross validation and models were trained using various training parameters. The final model was obtaining by averaging the models from three-fold cross validation D) Network architecture E) Results of evaluation using standard classification metrics. The final model achieved an accuracy of 92.3% in the unseen test set (n = 78).