Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images

Author:

Simić Svetlana1,Simić Svetislav D2,Banković Zorana3,Ivkov-Simić Milana1,Villar José R4,Simić Dragan2

Affiliation:

1. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 1–9, 21000 Novi Sad, Serbia

2. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia

3. Frontiers Media SA, Paseo de Castellana 77, Madrid, Spain

4. University of Oviedo, Campus de Llamaquique, 33005 Oviedo, Spain

Abstract

Abstract The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.

Funder

Ministry of Education, Science and Technological Development, Republic of Serbia

Asturias Regional Government

Spanish Ministry of Science and Innovation

Publisher

Oxford University Press (OUP)

Subject

Logic

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