2025

Comparison of CNN and Transformer Models for Predicting the Effects of Anti-VEGF Drugs on Ovarian Cancer from Histopathology Images

Galib Muhammad Shahriar Himel, Munim Ahmed, Shamsun Nahar Shatabdy, Md Sahilur Rahman, Md Shakhawat Hossain, MM Mahbubul Syeed and Mohammad Faisal Uddin

2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2025) 27-28 June 2025, Gazipur-1707, Bangladesh

View Publication DOI: 10.1109/NCIM65934.2025.11160069

Abstract

Anti-vascular endothelial growth factor (anti-
VEGF) therapy, such as Bevacizumab, treats colorectal, lung,
kidney, and breast cancer patients. In 2018, it was approved for
treating ovarian cancer (OC) patients; however, when administered, it results in some adverse effects. Therefore, this therapy is given to only selected OC patients. Traditionally, the selection is
done by manually examining the histopathology specimens of pa-
patients, which is time-consuming and vulnerable to inter-observer
variability. An AI-based method could be beneficial in fixing
these issues. In this study, we analyzed the suitability of popular
AI-based models for predicting the effect of anti-VEGF therapy
by analyzing patients’ histopathology images. We experimented
with seven popular convolutional neural network (CNN) based
models (VGG16, VGG19, ResNet50, InceptionNetV3, Xception,
MobileNet and DenseNet12) and six transformer models (ViT-
16, ViT-32, ViT-MaE, DiT, VAN and BEiT). We also investigated
the role of image magnification in training the models and
proposed a pipeline for automated patient selection leveraging
the histopathology whole slide images. Our study finds that the
transformer-based models achieve higher accuracy when trained
with a sufficiently large dataset than the CNN-based models.
The accuracy of the models was higher when trained using
20× images than 10×. The ViT-16 model achieved the highest
accuracy (98.6%) and area under the curve score (AUC) (99.8%)
for 20× images.

Citation

Galib Muhammad Shahriar Himel, Munim Ahmed, Shamsun Nahar Shatabdy, Md Sahilur Rahman, Md Shakhawat Hossain, MM Mahbubul Syeed and Mohammad Faisal Uddin. "Comparison of CNN and Transformer Models for Predicting the Effects of Anti-VEGF Drugs on Ovarian Cancer from Histopathology Images." 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2025) 27-28 June 2025, Gazipur-1707, Bangladesh (2025).

BibTeX

@article{pub47_2025,
  title={Comparison of CNN and Transformer Models for Predicting the Effects of Anti-VEGF Drugs on Ovarian Cancer from Histopathology Images},
  author={Galib Muhammad Shahriar Himel, Munim Ahmed, Shamsun Nahar Shatabdy, Md Sahilur Rahman, Md Shakhawat Hossain, MM Mahbubul Syeed and Mohammad Faisal Uddin},
  booktitle={2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2025) 27-28 June 2025, Gazipur-1707, Bangladesh},
  year={2025},
  doi={10.1109/NCIM65934.2025.11160069}
}

Publication Details

Type:

Year:
2025

Conference:
2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2025) 27-28 June 2025, Gazipur-1707, Bangladesh