Cervical Net: An Effective Convolution Neural Network for Five-class Classification of Cervical Cells

Cervical cancer continues to be a significant worldwide health issue, hence requiring the implementation of sophisticated diagnostic methods in order to improve the accuracy of detection. In this research, an effective CNN model is proposed for the classification of cervical cancer into five disease classes. Dyskeratotic cells are indicative of possible genetic abnormalities, while Koilocytotic cells are associated with the presence of HPV infection. Metaplastic cells are indicative of alterations in tissue structure, while Parabasal cells imply the presence of early-stage problems. Superficial-Intermediate cells offer valuable insights into the process of cellular differentiation. The proposed technology not only assists medical professionals in the identification of distinct cancer cell types, but also contributes to the advancement of focused treatment approaches, which could potentially enhance patient prognosis. Furthermore, the use of automated algorithms into the diagnostic procedure has the potential to improve efficiency and accessibility, particularly in settings with limited resources. In brief, the implementation of the five-class categorization system for cervical cancer holds significant potential in transforming the accuracy of diagnosis, thereby facilitating the development of more efficient interventions and ultimately mitigating the worldwide impact of cervical cancer.