Version 1
: Received: 7 May 2024 / Approved: 8 May 2024 / Online: 8 May 2024 (09:33:40 CEST)
How to cite:
Ruiz-Casado, J. L.; Molina-Cabello, M. A.; Luque-Baena, R. M. Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. Preprints2024, 2024050467. https://doi.org/10.20944/preprints202405.0467.v1
Ruiz-Casado, J. L.; Molina-Cabello, M. A.; Luque-Baena, R. M. Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. Preprints 2024, 2024050467. https://doi.org/10.20944/preprints202405.0467.v1
Ruiz-Casado, J. L.; Molina-Cabello, M. A.; Luque-Baena, R. M. Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. Preprints2024, 2024050467. https://doi.org/10.20944/preprints202405.0467.v1
APA Style
Ruiz-Casado, J. L., Molina-Cabello, M. A., & Luque-Baena, R. M. (2024). Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks. Preprints. https://doi.org/10.20944/preprints202405.0467.v1
Chicago/Turabian Style
Ruiz-Casado, J. L., Miguel A. Molina-Cabello and Rafael M. Luque-Baena. 2024 "Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks" Preprints. https://doi.org/10.20944/preprints202405.0467.v1
Abstract
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibilities used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalisability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations, such as translation or rotation, to the images to increase variability in the dataset. Recently, Generative Adversarial Networks (GANs) have emerged, which can generate images from a relatively small training set. This work aims to enhance the model’s performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.
Keywords
generative adversarial networks; data augmentation; classification; histopathological images; breast cancer
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
