A Deep Convolutional Neural Network Approach for Detecting Malignancy of Ovarian Cancer Using Densenet Model

Abstract

Ovarian malignant development has a helpless perseverance rate since general analysis and improved methods are needed for its underlying revelation. Ovarian danger is the sixth most characteristic infection in ladies, causing 152,000 decrease by and large yearly. To lessen this rate there is a requirement for an early detection of the infection. We plan a deep learning approach for essential recognition of ovarian malignancy utilizing histopathological pictures. Convolutional Neural Network (CNN) is utilized for the process. Feature extraction is finished by utilizing DenseNet-201 model of CNN. Malignant and benign cancer types are classified. In the characterization cycle, the PLCO dataset is utilized with most elevated ac-curacy accomplished is 94.73, exactness is 0.91, review and f1-score is 0.90 and 0.95 separately. Conditional outcomes and assessment of other earlier work explains truly trustworthy execution and the profitability of proposed work.