Detection of Ovarian Cancer Using Improved Deep Learning Model

Ovarian cancer (OC), the most common kind, accounts for more than half of all cases of gynecological cancer in women. The classification of OC might result in several distinct diagnoses (Serous, Mucinous, Endometrioid, Clear Cell). Pathologists use computer-aided diagnosis to assist them make accurate diagnoses. Deep convolutional neural networks (DCNNs) that have previously been trained can recognize, forecast, and classify the different kinds of ovarian cancer. An improved VGG-16 algorithmic structure contains thirteen convolution layers, three of which are linked. In addition, there are five maximum pooling layers and one softmax layer. After capturing 500 images, the model was only recognized with a 50% accuracy rate after training (100 from each class). Using a variety of image processing techniques, we were able to produce a total of 24742 more images from the initial dataset of 500 shots. Only after training on a much bigger dataset did the model’s accuracy increase from 50 to 84%. For the first time, VGG16 histopathological scans are being utilized to diagnose and forecast cancer ovarian tissue.