Image classi cation is an important task in many medical
applications, in order to achieve an adequate diagnostic of di erent le-
sions. Melanoma is a frequent kind of skin cancer, which most of them
can be detected by visual exploration. Heterogeneity and database size
are the most important di culties to overcome in order to obtain a good
classi cation performance. In this work, a deep learning based method
for accurate classi cation of wound regions is proposed. Raw images are
fed into a Convolutional Neural Network (CNN) producing a probability
of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were
used due to their well-known e ectiveness. Moreover, data augmentation
was used to increase the number of input images. Experiments show that
the compared models can achieve high performance in terms of mean ac-
curacy with very few data and without any preprocessing.
Buscar en Dimension