Deep Learning-Based Automated Detection and Classification of Ovarian Cancer from Medical Imaging

Ovarian cancer remains one of the most lethal types of gynaecological cancer because it advances asymptomatically and is often identified at an advanced stage. Early and precise identification is a critical to maintain health. Recent advancements in artificial intelligence (AI), especially deep learning (DL), have shown significant potential for enhancing computer-aided diagnosis and medical picture analysis. This study presents a DL-based system for the automated detection and classification of ovarian cancer utilising medical imaging techniques. The extraction of distinguishing features and categorisation of benign and malignant cases with high precision is achieved through the use of convolutional neural networks (CNNs) and transfer learning techniques. The proposed approach is evaluated using publicly accessible datasets and clinical datasets, demonstrating superior performance relative to traditional machine learning (ML) methods in accuracy, sensitivity, and specificity. DL models possess the capability to serve as valuable, dependable, and scalable instruments that can aid radiologists in the early detection and classification of ovarian cancer. This will ultimately enhance patient outcomes and facilitate tailored treatment planning. The results underscore the potential of deep learning methods.