Optimized Detection of Ovarian Cancer Using Segmentation with FR-CNN Classification

This paper proposes a novel annotated image classification utilizing FR-CNN (faster region-based convolutional neural network) depending on ROI (region of interest). In this, input images were classified into three kinds called, epithelial, gem cell and stroma cell. This image is preprocessed and segmented utilizing ROI. Afterward, the process of annotation receipts is placed through the FR-CNN. This framework contrasts the manual features of annotation and features are trained in FRCNN for region-based classification. Disease detection with higher accuracy is analyzed by FRCNN as the manual annotation with lower accuracy in the existing works and this work mathematically proved that the classification dependent on machine learning produces high accuracy.