ABCD rule and pre-trained CNNs for melanoma diagnosis

• Published in: Multimedia tools and applications Vol. 78; no. 6; pp. 6869 - 6888
• Main Authors: Moura, Nayara, Veras, Rodrigo, Aires, Kelson, Machado, Vinícius, Silva, Romuere, Araújo, Flávio, Claro, Maíla
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-03-2019; Springer Nature B.V
• Subjects: Artificial neural networks; Cancer; Computation; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diagnosis; Lesions; Medical imaging; Melanoma; Multilayer perceptrons; Multimedia Information Systems; Neural networks; Skin cancer; Special Purpose and Application-Based Systems; Multilayer perceptron; Attribute selection; ABCD rule; Pre-trained CNNs; Medical image classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6404-8

Skin cancer is the most common type of cancer and represents more than half of cancer diagnoses. Melanoma is the least frequent among skin cancers, but it is the most serious, with high potential for metastasis and can lead to death. However, melanoma is almost always curable if discovered in the early stages. In this context, computational methods for processing and analysis of skin lesion images have been studied and developed. This work proposes a computational approach to assist dermatologists in the diagnosis of skin lesions in melanoma or non-melanoma by means of dermoscopic images. The proposed methodology classifies skin lesions using a descriptor formed by the combination of the ABCD rule (Asymmetry, Border, Color, and Diameter) and pre-trained Convolutional Neural Networks (CNNs) features. The features were selected according to their gain ratios and used as input to the MultiLayer Perceptron classifier. We built a new database joining two distinct databases presented in the literature to validate the proposed methodology. The proposed method achieved an accuracy rate of 94.9% and Kappa index of 89.2%, which is considered “excellent”.