Retinoblastoma Detection: Leveraging Deep Learning and Residual Connections for Enhanced Diagnostic Accuracy

Shuaa S. Alharbi

2025

Abstract

Retinoblastoma is a rare cancer of the eye that affects children and can be deadly if not diagnosed in time. Detecting this disease early improves the likelihood of curative treatment and makes it possible to preserve the child’s vision. Meanwhile, the application of deep learning techniques to pathology holds the promise of revolutionizing cancer detection and treatment early. When it comes to retinoblastoma, the prospect of automating diagnostic processes to work more accurately and efficiently than healthcare workers can detect dangerous cases with better-than-average accuracy should improve survival rates, as well as rates of vision conservation. In this study, we evaluated several convolutional neural network models: MobileNetV2, EfficientNetB0, ResNet101, DenseNet121, VGG16, and an ensemble model providing a quantitive comparison of which of the models performs best. Among the models, the one that performed best and most accurately was ResNet101, which achieved an accuracy of 97.42%(top-1 accuracy). Comparatively, EfficientNetB0 had a lower metric that indicated its accuracy was 53.40% (top-1 accuracy). ResNet101’s relatively high accuracy for this study suggests that this model is better suited for this type of feature-based classification problem compared to the other models. Residual connection blocks allow layers in a deep neural network to learn to map the input to the same output. This improves performance and reduces errors. Residual networks (ResNets) with many layers have now become the standard architecture used in the leading vision challenges, which gives more insight to researchers and practitioners in choosing the most suitable diagnostic model.

Paper Citation

in Harvard Style

Alharbi S. (2025). Retinoblastoma Detection: Leveraging Deep Learning and Residual Connections for Enhanced Diagnostic Accuracy. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 269-279. DOI: 10.5220/0013099000003911

in Bibtex Style

@conference{bioimaging25,
author={Shuaa Alharbi},
title={Retinoblastoma Detection: Leveraging Deep Learning and Residual Connections for Enhanced Diagnostic Accuracy},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={269-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013099000003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Retinoblastoma Detection: Leveraging Deep Learning and Residual Connections for Enhanced Diagnostic Accuracy
SN - 978-989-758-731-3
AU - Alharbi S.
PY - 2025
SP - 269
EP - 279
DO - 10.5220/0013099000003911
PB - SciTePress