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<p>The neoplasm of the cervix, the second most ubiquitous malignancy affecting the female population, originates from anomalous cellular proliferation within the uterine cervix, a vital morphological component within the uterine cavity. Identifying it early cannot be emphasized enough, leading to the utilization of diverse screening modalities, including Cervical screenings, colposcopic examination, and HPV screening, to pinpoint possible side effects and facilitate prompt intervention. These screening protocols involve a range of diagnostic modalities, including visual examinations, cervical cytology, colposcopic examinations, tissue sampling, and human papillomavirus testing, which require the expertise and specialized proficiency of experienced physicians and pathologists, considering the complex and nuanced characteristics of neoplasm of the cervix diagnosis, which demands a high degree of knowledge and specificity nuanced and cancer diagnosis can be impacted by individual expertise and perspective. Addressing the pressing need for more innovative and more efficient cancer screening, this article offers a groundbreaking solution that presents a pioneering approach that harnesses the power of pre-trained neural network architecture to extract features. The models were refined through fine-tuning complemented by incorporating Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) to form Deep Network for Cervical Cancer Location (DNCCL). This robust Predictive analytics algorithm was used to enhance the neoplasm of the cervix diagnosis precision, demonstrating an outstanding success rate yielding and obtaining a precision rate of 98.08%, an awe-inspiring achievement. The research was facilitated by the SIPaKMeD dataset, which is freely accessible in this investigation and substantively adds to the existing body&#xa0;of&#xa0;research, significantly enhancing the transparency and reproducibility of our research outcomes. A novel combined methodology is proposed for the neoplasm of the cervix classification, leveraging the complementary strengths and synergies between neural networks and predictive analytics. Even the most delicate and intricate features can be accurately extracted from pictures through DL. Moreover, the extracted features can be leveraged as colposcopic images for benchmarking and optimizing diverse algorithms and predictive analytics to strengthen predictive capabilities. This DNCCL methodology that goes beyond shows provides a promising solution for substantially enhancing neoplasm of the cervix detection is just one application; our approach also highlights the revolutionary opportunities presented by intelligent automation in medical diagnostics, enabling more precise, allowing for early and accurate interventions.</p>

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Modelling a novel approach for cervical cancer prediction using pre-trained network model

  • 期刊论文

  • 发表日期：

    2025年11月17日

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  • S. Devi,
  • Vijayakumar Adaickalam

摘要

The neoplasm of the cervix, the second most ubiquitous malignancy affecting the female population, originates from anomalous cellular proliferation within the uterine cervix, a vital morphological component within the uterine cavity. Identifying it early cannot be emphasized enough, leading to the utilization of diverse screening modalities, including Cervical screenings, colposcopic examination, and HPV screening, to pinpoint possible side effects and facilitate prompt intervention. These screening protocols involve a range of diagnostic modalities, including visual examinations, cervical cytology, colposcopic examinations, tissue sampling, and human papillomavirus testing, which require the expertise and specialized proficiency of experienced physicians and pathologists, considering the complex and nuanced characteristics of neoplasm of the cervix diagnosis, which demands a high degree of knowledge and specificity nuanced and cancer diagnosis can be impacted by individual expertise and perspective. Addressing the pressing need for more innovative and more efficient cancer screening, this article offers a groundbreaking solution that presents a pioneering approach that harnesses the power of pre-trained neural network architecture to extract features. The models were refined through fine-tuning complemented by incorporating Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) to form Deep Network for Cervical Cancer Location (DNCCL). This robust Predictive analytics algorithm was used to enhance the neoplasm of the cervix diagnosis precision, demonstrating an outstanding success rate yielding and obtaining a precision rate of 98.08%, an awe-inspiring achievement. The research was facilitated by the SIPaKMeD dataset, which is freely accessible in this investigation and substantively adds to the existing body of research, significantly enhancing the transparency and reproducibility of our research outcomes. A novel combined methodology is proposed for the neoplasm of the cervix classification, leveraging the complementary strengths and synergies between neural networks and predictive analytics. Even the most delicate and intricate features can be accurately extracted from pictures through DL. Moreover, the extracted features can be leveraged as colposcopic images for benchmarking and optimizing diverse algorithms and predictive analytics to strengthen predictive capabilities. This DNCCL methodology that goes beyond shows provides a promising solution for substantially enhancing neoplasm of the cervix detection is just one application; our approach also highlights the revolutionary opportunities presented by intelligent automation in medical diagnostics, enabling more precise, allowing for early and accurate interventions.