Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models-Xception, ResNet50V2, and ResNet50V2FPN-to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model's performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods. This research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.