Cervical cancer detection and segmentation using ANFIS classifier and prediction by ensemble deep learning network
D. Baskar, K. Manivanan · International Journal of Medical Engineering and Informatics · 2024
In underdeveloped nations, the incidence frequencies of cervical cancer have been sharply rising while the health services for prevention, diagnosis, as well as therapy are still relatively few. Cancer screening procedures may lead to an early diagnosis, which increases the likelihood of successful treatment and, ultimately, the protection of cervical cancer. An approach for the identification, as well as fragmentation of cervical cancer depending on the adaptive neuro-fuzzy inference system (ANFIS), is provided here. A novel model has been built and given the name the colposcopy ensemble network (CYENET) to automatically diagnose cervical malignancies from colposcopy pictures. The cell classification feature is dependent on a compact vision geometry grouping (VGG) network termed compact VGG. The overall accuracy of the classification for VGG19 was 73.31%. The findings of the experiments indicate that the suggested CYENET displayed high levels of sensitivity (92.42%), specificity (96.22%), and kappa scores (88%).