2026-05-27T21:06:58Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/227172026-02-03T12:16:00Zcom_10630_2254col_10630_37959

Thurnhofer Hemsi, Karl Maza Quiroga, Rosa María Domínguez-Merino, Enrique Molina-Cabello, Miguel Ángel López-Rubio, Ezequiel 2021-07-30T08:27:28Z 2021-07-30T08:27:28Z 2021 https://hdl.handle.net/10630/22717 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. eng open access Ciencias de la computación Lenguaje de computación Programación Cáncer de piel Diagnóstico médico Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment conference output