Automatic labeling of fish species using deep learning across different classification strategies - Jareño, Javier; Bárcena-González, Guillermo; Castro-Gutiérrez, Jairo; Cabrera-Castro, Remedios; Galindo, Pedro L. (Frontiers in Computer Science, 2024)
Convolutional neural networks (CNNs) have revolutionised image recognition. Their ability to identify complex patterns, combined with learning transfer techniques, has proven effective in multiple fields, such as image classification. In this paper we propose to apply a two-step methodology for image classification tasks. First, apply transfer learning with the desired dataset, and subsequently, in a second stage, replace the classification layers by other alternative classification models. The whole methodology has been tested on a dataset collected at Conil de la Frontera fish market, in Southwest Spain, including 19 different fish species to be classified for fish auction market. The study was conducted in five steps: (i) collecting and preprocessing images included in the dataset, (ii) using transfer learning from 4 well-known CNNs(ResNet152V2, VGG16, EfficientNetV2L and Xception) for image classification to get initial models, (iii) apply fine-tuning to obtain final CNN models (iv) substitute classification layer with 21 different classifiers obtaining multiple F1-scores for different training-test splits of the dataset for each model and (v) apply post-hoc statistical analysis to compare their performances in terms of accuracy. Results indicate that combining the feature extraction capabilities of CNNs with other supervised classification algorithms, such as Support Vector Machines or Linear Discriminant Analysis is a simple and effective way to increase model performance.