Different oncologists make their own decisions about the detection and classification of the type
of ovarian cancer from histopathological whole slide images. However, it is necessary to have an
automated system that is more accurate and standardized for decision-making, which is essential for
early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian
cancer system is proposed. This model starts by extracting the main features from the histopathology
images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature
elimination based on a decision tree is introduced to remove unnecessary features extracted during
the feature extraction process. Adam optimizers were implemented to optimize the network’s weights
during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined
to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E)
stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images
(WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post
treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of
98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising
results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer.