A multilevel features selection framework for skin lesion classification

Melanoma is considered to be one of the deadliest skin cancer types, whose occurring frequency elevated in the last few years; its earlier diagnosis, however, significantly increases the chances of patients' survival. In the quest for the same, a few computer based methods, capable of diagnosing the skin lesion at initial stages, have been recently proposed. Despite some success, however, margin exists, due to which the machine learning community still considers this an outstanding research challenge. In this work, we come up with a novel framework for skin lesion classification, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving the original feature space. We utilize recent deep models for feature extraction, and by taking advantage of transfer learning. Initially, the dermoscopic images are segmented, and the lesion region is extracted, which is later subjected to retrain the selected deep models to generate fused feature vectors. In the second phase, a framework for most discriminant feature selection and dimensionality reduction is proposed, entropy-controlled neighborhood component analysis (ECNCA). This hierarchical framework optimizes fused features by selecting the principle components and extricating the redundant and irrelevant data. The effectiveness of our design is validated on four benchmark dermoscopic datasets; PH2, ISIC MSK, ISIC UDA, and ISBI-2017. To authenticate the proposed method, a fair comparison with the existing techniques is also provided. The simulation results clearly show that the proposed design is accurate enough to categorize the skin lesion with 98.8%, 99.2% and 97.1% and 95.9% accuracy with the selected classifiers on all four datasets, and by utilizing less than 3% features.