Advancing Cervical Cancer Diagnosis Through Deep Learning: Architectures, Challenges, and Future Directions

Abstract

Abstract: The abstract highlights the significance of deep learning (DL) technology in addressing cervical cancer (CC), a leading cause of female mortality globally. With over 700 daily fatalities and an estimated 400,000 annual deaths by 2030, early detection is imperative. DL techniques offer accurate diagnoses, thereby improving treatment outcomes. The project integrates various DL models, including CNN, DenseNet, and Xception, for feature extraction, enabling the development of robust classification models such as SVM, KNN, Bayesian Networks, Decision Trees, and MLP. Additionally, DL-based detection techniques using YoloV5 and YoloV8 are explored for CC analysis. The utilization of these models significantly enhances diagnostic accuracy, with CNN and SVM achieving 99% accuracy in the base paper. The project's extension further improves performance by incorporating YoloV5 and YoloV8 for detection tasks, enhancing the system's capability to detect CC accurately. The implications of this project extend beyond improved diagnosis, benefiting women globally, particularly in low-income countries, by reducing morbidity and mortality rates. Healthcare professionals gain access to efficient diagnostic tools, enabling timely interventions and personalized therapy for better patient outcomes. Overall, the project underscores the pivotal role of DL technology in combating CC and improving healthcare outcomes.