Comparative Analysis of CNN Performance in Keras, PyTorch and JAX on PathMNIST
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915393470201856 |
|---|---|
| author | Nezović, Anida Romano, Jalal Marić, Nada Kapo, Medina Akagić, Amila |
| author_facet | Nezović, Anida Romano, Jalal Marić, Nada Kapo, Medina Akagić, Amila |
| contents | Deep learning has significantly advanced the field of medical image classification, particularly with the adoption of Convolutional Neural Networks (CNNs). Various deep learning frameworks such as Keras, PyTorch and JAX offer unique advantages in model development and deployment. However, their comparative performance in medical imaging tasks remains underexplored. This study presents a comprehensive analysis of CNN implementations across these frameworks, using the PathMNIST dataset as a benchmark. We evaluate training efficiency, classification accuracy and inference speed to assess their suitability for real-world applications. Our findings highlight the trade-offs between computational speed and model accuracy, offering valuable insights for researchers and practitioners in medical image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12248 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of CNN Performance in Keras, PyTorch and JAX on PathMNIST Nezović, Anida Romano, Jalal Marić, Nada Kapo, Medina Akagić, Amila Computer Vision and Pattern Recognition Machine Learning Deep learning has significantly advanced the field of medical image classification, particularly with the adoption of Convolutional Neural Networks (CNNs). Various deep learning frameworks such as Keras, PyTorch and JAX offer unique advantages in model development and deployment. However, their comparative performance in medical imaging tasks remains underexplored. This study presents a comprehensive analysis of CNN implementations across these frameworks, using the PathMNIST dataset as a benchmark. We evaluate training efficiency, classification accuracy and inference speed to assess their suitability for real-world applications. Our findings highlight the trade-offs between computational speed and model accuracy, offering valuable insights for researchers and practitioners in medical image analysis. |
| title | Comparative Analysis of CNN Performance in Keras, PyTorch and JAX on PathMNIST |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.12248 |