Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap images of the cervical region are employed to detect classify abnormality tissues. In this paper, we proposed first system that it ables into a seven classes problem. exploited design computer-aided diagnoses in cells. Automated features have been extracted using ResNet101 discriminate Support Vector Machine (SVM) classifier. The success distinguishing between levels normal cases with 100% accuracy sensitivity. On top that, can distinguish abnormal an 100%. high level then studied classified accuracy. other hand, low separately two classes, mild moderate dysplasia, ∼ 92% built-in cascading manner five models polynomial overall training all 100%, while test around phase reaches 97.3%. facilitates process cells leads early diagnosis cancer, which may lead increase survival rate women.