Automated Bubble Detection in Contact Lenses Using a Hybrid Deep Learning Framework

This study presents a hybrid deep learning approach for automated detection of bubbles in contact lenses, aiming to enhance quality assurance in the manufacturing process. A hybrid AlexNet+SVM model was developed using transfer learning, where AlexNet’s convolutional features were leveraged for binary classification (bubble vs. normal) via a Support Vector Machine (SVM) classifier. The dataset consisted of 320 images (160 bubbles, 160 normal) pre-processed using median filtering, local histogram equalization, and circular masking to improve image clarity and consistency. Through systematic hyperparameter tuning, the model achieved 100% testing accuracy and 97.92% validation accuracy, with perfect precision (100%) and high recall (96%). Comparative evaluation against ResNet and VGGNet demonstrated that the AlexNet+SVM model offered superior generalization and robustness, particularly for small-scale datasets. While VGGNet also achieved 100% testing accuracy with 95.83% validation accuracy, ResNet underperformed in recall (89%), likely due to its deeper architecture and data limitations. The findings underscore the suitability of hybrid models for binary classification tasks in limited-data scenarios. Identified challenges, including dataset size and risk of overfitting, point to future research directions involving expanded datasets and more advanced pre-processing techniques. This research contributes to the advancement of automated defect detection systems for contact lens manufacturing, offering a reliable and efficient quality control solution.

@article{Lim2025,
  title     = {Automated Bubble Detection in Contact Lenses Using a Hybrid Deep Learning Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Chee Chin Lim and Yen Fook Chong and Vikneswaran Vijean and Gei Ki Tang},
  doi       = {10.14569/IJACSA.2025.0160773},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160773}
}