Thurnhofer-Hemsi, KarlLópez-Rubio, EzequielDomínguez, EnriqueElizondo, David A.2022-10-282022-10-282021-08-09Thurnhofer-Hemsi K, López-Rubio E, Domínguez E, Elizondo DA. Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting. 2021; 9:112193-112205http://hdl.handle.net/10668/4298Skin 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.spaAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Image processingDeep learningClassificationSkin lesionMelanomaConvolutional neural networksSkin cancerProcesamiento de imagen asistido por computadorAprendizaje profundoClasificaciónLesiones por desenguantamientoRed nerviosaNeoplasias cutáneasMedical Subject Headings::Diseases::Neoplasms::Neoplasms by Histologic Type::Nevi and Melanomas::MelanomaMedical Subject Headings::Diseases::Neoplasms::Neoplasms by Site::Skin NeoplasmsMedical Subject Headings::Phenomena and Processes::Mathematical Concepts::Neural Networks (Computer)Medical Subject Headings::Diseases::Skin and Connective Tissue Diseases::Skin DiseasesMedical Subject Headings::Diseases::Immune System Diseases::HypersensitivityMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Anatomy::Cells::Epithelial Cells::MelanocytesMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::ProbabilityMedical Subject Headings::Anatomy::Integumentary System::Skin::EpidermisMedical Subject Headings::Information Science::Information Science::Computing Methodologies::Image Processing, Computer-AssistedMedical Subject Headings::Information Science::Information Science::ClassificationMedical Subject Headings::Diseases::Skin and Connective Tissue Diseases::Skin DiseasesSkin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shiftingreview articleAcceso abierto2169-3536