ABC-Optimized CNN-GRU Algorithm for Improved Cervical Cancer Detection and Classification Using Multimodal Data

Cervical cancer is the second most common malignancy among women, making it a major public health problem worldwide. Early detection of cervical cancer is important because it increases the chances of effective treatment and survival. Regular screening and early management can prevent the growth of cervical cancer, thus reducing mortality. Traditional methods of detection, such as Pap smears, have proven useful, but are time-consuming and rely on behavioral interpretation by cytologists. To overcome these issues the study uses method another for a convolutional neural networks (CNNs) and gated recurrent units (GRUs) to detect and classify cervical cancer in Pap smear images by tuning with Artificial Bee Colony (ABC) Optimizer. This study used several datasets with high-resolution images from the SipakMed collection, with 4049 images and a fetal dataset with patient information for the CNN component of the model, specifically the ResNet-152 system, is extracted spatial attributes from these images. After feature extraction, the GRU component analyzes the sequential data to identify temporal combinations and patterns. This hybrid CNN-GRU algorithm uses the features of two networks: the ability of CNN to learn spatial patterns and the ability of GRU to understand sequential networks and tuning the parameters using ABC. The proposed model outperformed the conventional ML methods with a classification accuracy of 94.89%, and provided a reliable solution for early detection of cervical cancer Using these DL methods role which, not only enables a more accurate diagnosis, but also allows a comprehensive examination of the abnormal cervical cells, making it a positive detections to programs and patient outcomes. This work highlights the promise of cutting-edge AI techniques to improve cervical cancer diagnosis, and the need for faster and more accurate diagnosis in the battle to emphasize the fight against this common disease.

@article{Rohini2024,
  title     = {ABC-Optimized CNN-GRU Algorithm for Improved Cervical Cancer Detection and Classification Using Multimodal Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Donepudi Rohini and M Kavitha},
  doi       = {10.14569/IJACSA.2024.0150971},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150971}
}