Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer

• Published in: Multimedia tools and applications 2020-11, Vol.79 (41-42), p.31219-31238
• Main Authors: Filali, Youssef, EL Khoukhi, Hasnae, Sabri, My Abdelouahed, Aarab, Abdellah
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Multimedia Information Systems; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09637-4

Skin cancer is one of the most aggressive cancers in the world. Computer-Aided Diagnosis (CAD) system for cancer detection and classification is a top-rated solution that decreases human effort and time with very high classification accuracy. Machine learning (ML) and deep learning (DL) based approaches have been widely used to develop robust skin-lesion classification systems. Each of the techniques excels when the other fails. Their performances are closely related to the size of the learning dataset. Thus, approaches that are based on the ML are less potent than those found on the DL when working with large datasets and vice versa. In this article, we propose a powerful skin-lesion classification approach based on a fusion of handcrafted features (shape, skeleton, color, and texture) and features extracted from most powerful DL architectures. This combination will make it possible to remedy the limitations of both the ML and DL approaches for the case of large and small datasets. Features engineering is then applied to remove redundant features and to select only relevant features. The proposed approach is validated and tested on both small and large datasets. A comparative study is also conducted to compare the proposed approach with different and recent approaches applied to each dataset. The results obtained show that this features-fusion based approach is very promising and can effectively combine the power of ML and DL based approaches.