A multi-level closing based segmentation framework for dermatoscopic images using ensemble deep network.

Abstract

Abstract The proposed framework is a hybrid model of extensive multi-level closing based hair removal pre-processing followed by training using an ensemble deep network. In this way, a highly optimised pedagogy for lesion segmentation in dermatoscopic images has been obtained. Two publicly available datasets are then used to analyse the performance of the framework. One is HAM10k dataset and another is ISIC dataset. The segmented images are compared with the mask given with the dataset and accordingly the value of Dice Coefficient, Jaccard Similarity index and other performance metrics are computed. The average values of Dice Coefficient and Jaccard value for both datasets are found to be 0.9555 and 0.8545 respectively. These values along with other performance metrics are compared with values of base models and state of the art techniques and was found to be better. The proposed framework achieved an average accuracy of 95.87% for both datasets which is better than all base models and even better than the proposed framework without pre-processing.