To differentiate between the resinous compound and the decayed core regions, which share similar visual characteristics, the proposed method can be extended by incorporating additional image processing techniques and deep learning models. One approach could involve training a separate deep learning model specifically for distinguishing between these two regions. This model could be trained on a dataset that includes examples of both resinous compound and decayed core regions with varying visual characteristics. By leveraging a more complex model architecture, such as a combination of CNNs and RNNs for sequential data analysis, the system can learn intricate patterns and textures that differentiate between the two regions. Additionally, incorporating advanced image processing algorithms like texture analysis and edge detection can provide valuable features to aid in the classification process. By integrating these techniques, the system can achieve a higher level of accuracy in segmenting and classifying the resinous compound and decayed core regions.