RT Journal Article
T1 Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting
A1 Thurnhofer-Hemsi, Karl
A1 López-Rubio, Ezequiel
A1 Domínguez, Enrique
A1 Elizondo, David A.
K1 Image processing
K1 Deep learning
K1 Classification
K1 Skin lesion
K1 Melanoma
K1 Convolutional neural networks
K1 Skin cancer
K1 Procesamiento de imagen asistido por computador
K1 Aprendizaje profundo
K1 Clasificación
K1 Lesiones por desenguantamiento
K1 Red nerviosa
K1 Neoplasias cutáneas
AB Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification is an essential task to achieve an adequate diagnostic of different lesions. Melanoma is one of the best-known types of skin lesions due to the vast majority of skin cancer deaths. In this work, we propose an ensemble of improved convolutional neural networks combined with a test-time regularly spaced shifting technique for skin lesion classification. The shifting technique builds several versions of the test input image, which are shifted by displacement vectors that lie on a regular lattice in the plane of possible shifts. These shifted versions of the test image are subsequently passed on to each of the classifiers of an ensemble. Finally, all the outputs from the classifiers are combined to yield the final result. Experiment results show a significant improvement on the well-known HAM10000 dataset in terms of accuracy and F-score. In particular, it is demonstrated that our combination of ensembles with test-time regularly spaced shifting yields better performance than any of the two methods when applied alone.
PB Institute of Electrical and Electronics Engineers
YR 2021
FD 2021-08-09
LK http://hdl.handle.net/10668/4298
UL http://hdl.handle.net/10668/4298
LA spa
NO Thurnhofer-Hemsi K, López-Rubio E, Domínguez E, Elizondo DA. Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting. 2021; 9:112193-112205
DS RISalud
RD Apr 9, 2025