Deep Convolutional Neural Network Ensembles For Multi-Classification of Skin Lesions From Dermoscopic and Clinical Images

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Reisinho, J (Author) Other; Coimbra, M (Author) FCUP; Renna, F (Author) FCUP

Conference proceedings International

Title: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2020, Montreal, QC, Canada, July 20-24, 2020

Pages: 1940-1943

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00S-S4F

DOI: 10.1109/embc44109.2020.9176411

Abstract (EN): In this paper, we consider the problem of classifying skin lesions into multiple classes using both dermoscopic and clinical images. Different convolutional neural network architectures are considered for this task and a novel ensemble scheme is proposed, which makes use of a progressive transfer learning strategy. The proposed approach is tested over a dataset of 4000 images containing both dermoscopic and clinical examples and it is shown to achieve an average specificity of 93.3% and an average sensitivity of 79.9% in discriminating skin lesions belonging to four different classes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4