Optimizing cervical cancer diagnosis with accurate cell classification using modified HDFF.

Background: Cervical cancer (CC) is a leading cause of cancer-related deaths worldwide, emphasizing the need for accurate and efficient diagnostic tools. Traditional methods of cervical cell classification are time-consuming and susceptible to human error, highlighting the need for automated solutions. Materials and methods: This study introduces the modified hierarchical deep feature fusion (HDFF) method for cervical cell classification using the SIPaKMeD and Herlev datasets. The novelty of this research lies in the integration of hierarchical deep learning features, which allows for more accurate and robust classification. By enhancing the feature extraction process and combining multiple layers of deep learning models, the Modified HDFF method improves classification performance across various tasks, ranging from binary to multi-class problems. Results: Our results demonstrate that the Modified HDFF method significantly outperforms existing models. In the 2-class task, it achieves an impressive accuracy of 98.88%, surpassing other approaches such as RF-based hierarchical classification (98.43%). Additionally, it maintains high precision, recall, and F1-scores in multi-class tasks, with 98.8% accuracy in the 3-class problem and 98.5% in the 7-class problem. Conclusions: Overall, the Modified HDFF method shows great promise as a reliable and efficient diagnostic tool for cervical cancer screening. Its superior accuracy across multiple classification tasks highlights its potential for improving early detection and public health outcomes. Further refinement and expanded training datasets can further enhance its performance, making it an invaluable asset in automated cervical cancer detection.