A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets
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Miguel Ángel Molina [1] ; Gualberto Asencio-Cortés [1] ; Riquelme, José C. [2] ; Francisco Martínez- Álvarez [1]
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[1] Universidad Pablo de Olavide
Universidad Pablo de Olavide
Sevilla, España
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[2] Universidad de Sevilla
Universidad de Sevilla
Sevilla, España
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- Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío, Carlos Cambra Baseca, Daniel Urda Muñoz, Javier Sedano Franco, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-57802-2, págs. 741-750
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
A new transfer learning strategy is proposed for image classification in this work, based on an 8-layer convolutional neural network. The transfer learning process consists in a training phase of the neural network on a source dataset of images. Then, the last two layers are retrained using a different small target dataset of images. A preliminary study was conducted to train and test the transfer learning proposal on Malaria cell images for a binary classification problem. The methodology proposed has provided a 6.76% of improvement with respect to other three different strategies of training non-transfer learning models. The results achieved are quite promising and encourage to conduct further research in this field.
