Ovarian Cancer Subtypes Reimagined: Leveraging CNNs for Advanced Classification and Outlier Detection

Abstract

Ovarian cancer remains an aggressive challenge in the realm of cancer research and treatment due to its diverse subtypes, each characterized by distinct genetic and molecular features. The UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN) applies advanced deep learning techniques, particularly convolutional neural networks (CNNs), to address the complexities of ovarian cancer subtypes like CC, EC, HGSC, LGSC, and MC classification and outlier detection. A robust CNN-based model capable of accurately classifying different subtypes of ovarian cancer. Traditional methods often face challenges in handling the complicated patterns and subtle differences in medical imaging data. CNNs excel in image-based tasks by automatically learning hierarchical representations, making them ideal for the nuanced classification required in cancer subtype analysis. Subtype classification: the UBC-OCEAN incorporates outlier detection mechanisms to identify anomalies within the dataset. Outliers may represent rare or previously unidentified subtypes or instances of the disease that exhibit unique characteristics. Integration of advanced deep learning techniques in cancer research signifies a paradigm shift towards more sophisticated and efficient approaches in the quest for improved diagnostic accuracy and patient outcomes. The UBC-OCEAN represents a crucial step forward in harnessing the power of CNNs for the intricate task of ovarian cancer subtype classification and outlier detection.