A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets
- 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
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2
Universidad de Sevilla
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- Álvaro Herrero (coord.)
- Carlos Cambra (coord.)
- Daniel Urda (coord.)
- Javier Sedano (coord.)
- Héctor Quintián (coord.)
- Emilio Corchado (coord.)
Publisher: Springer Suiza
ISBN: 978-3-030-57801-5, 978-3-030-57802-2
Year of publication: 2021
Pages: 741-750
Congress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)
Type: Conference paper
Sustainable development goals
Abstract
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.