Abstract
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.
Highlights
Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]
3 We propose a hierarchical architecture for feature selection and dimensionality reduction, which in the initial step relies upon entropy for feature selection, followed by dimensionality reduction using neighborhood component analysis (NCA)
Three families of state-of-the-art classifiers are utilized for classification including K-nearest neighbor (KNN), support vector machine (SVM), and Ensemble (ES)
Summary
Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. It has been reported [2] that diagnosis of melanoma, in its early stages, significantly increases chances of the patient’s survival. (2020) 10:12 melanoma detection, in which physicians apply gel on the affected skin, prior to examining it with a dermoscope It allows recognition of sub-surface structures of the infected skin that are invisible to naked eye. With this clinical procedure, the skin lesion is amplified up to 100 times, thereby easing the examination [3]
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