RT journal article
T1 Automatic labeling of fish species using deep learning across different classification strategies
A1 Jareño Dorado, Javier
A1 Barcena González, Guillermo
A1 Castro-Gutiérrez, Jairo
A1 Cabrera Castro, Remedios
A1 Galindo Riaño, Pedro Luis
A2 Biología
A2 Ingeniería Informática
K1 supervised learning, classification, fish species, SVM, LDA, deep learning, multiple comparison analysis
K1 supervised learning
K1 classification
K1 fish species
K1 SVM
K1 LDA
K1 deep learning
K1 multiple comparison analysis
AB Convolutional neural networks (CNNs) have revolutionized 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 article we propose to apply a two-step methodology for image classification tasks. First, apply transfer learning with the desired dataset, and subsequently, ina 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, E cientNetV2L 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.
SN 2624-9898
YR 2024
FD 2024-02-29
LK http://hdl.handle.net/10498/31979
UL http://hdl.handle.net/10498/31979
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026