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Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment
dc.contributor.author | Molina-Cabello, Miguel Ángel | |
dc.contributor.author | Thurnhofer Hemsi, Karl | |
dc.contributor.author | Maza Quiroga, Rosa María | |
dc.contributor.author | Domínguez, Enrique | |
dc.contributor.author | López-Rubio, Ezequiel | |
dc.contributor.author | Molina-Cabello, Miguel Ángel | |
dc.date.accessioned | 2021-07-30T08:27:28Z | |
dc.date.available | 2021-07-30T08:27:28Z | |
dc.date.created | 2021 | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/10630/22717 | |
dc.description.abstract | Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as the ABCDE rule), but they depend mainly on the physician’s experience and might be imprecise. Deep learning models are very extended in medical image analysis, and several deep models have been proposed for moles classification. In this work, a convolutional neural network is proposed to support the diagnosis procedure. The proposed MobileNetV2-based model is improved by a shifting technique, providing better performance than raw transfer learning models for moles classification. Experiments show that this technique could be applied to the state-of-the-art deep models to improve their results and outperform the training phase. | es_ES |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Ciencias de la computación | es_ES |
dc.subject | Lenguaje de computación | es_ES |
dc.subject | Programación | es_ES |
dc.subject | Cancer de piel | es_ES |
dc.subject | Diagnóstico médico | es_ES |
dc.subject.other | Image processing | es_ES |
dc.subject.other | Deep learning | es_ES |
dc.subject.other | Classification | es_ES |
dc.subject.other | Skin lesion | es_ES |
dc.title | Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.centro | E.T.S.I. Informática | es_ES |
dc.relation.eventtitle | International Joint Conference on Neural Networks 2021 (IJCNN 2021) | es_ES |
dc.relation.eventplace | Virtual | es_ES |
dc.relation.eventdate | Julio de 2021 | es_ES |