An Efficient Deep Learning Framework for Automated Ovarian Cancer Detection

Deep learning-based analysis of medical imaging for ovarian cancer detection has attracted considerable attention in recent years because of its potential to support early diagnosis, improve accuracy, and enhance clinical outcomes. Among the different approaches, convolutional neural networks (CNNs) and their advanced architectures have proven to be highly effective for automated cancer detection. However, medical images are often complex, and variations in tumor shape, size, and texture can make accurate classification challenging. Therefore, it is important to understand how features are extracted and how model performance evolves during the detection process to improve overall system efficiency. In this study, a computational approach is used to investigate ovarian cancer detection through deep learning techniques. A robust CNN-based model is designed to classify histopathological images of ovarian tissue. The system considers important factors such as image preprocessing, feature extraction, model architecture, and training conditions to reflect realistic diagnostic scenarios. The performance of the proposed model is also compared with well-known deep learning architectures such as VGG16 and ResNet50, which are widely used in medical image analysis. The evaluation focuses on key performance metrics including classification accuracy, precision, recall, and training loss behavior. The results show that the choice of model architecture plays a significant role in feature learning and overall classification performance, leading to noticeable differences in detection accuracy. The outcomes of this study provide useful insights into how deep learning models behave in medical image classification tasks. These findings can help in optimizing model parameters for more accurate ovarian cancer detection. Ultimately, the proposed framework has the potential to support better diagnostic decisions, reduce misclassification, and improve the reliability of AI-based healthcare systems.