Award-winning research advances breast cancer image classification
Dr ArunaDevi's research addresses the critical challenge of histopathology image classification, essential for breast cancer diagnosis. While traditional methods rely on hand-crafted features, her study explores feed-forward approaches, proposing Convolutional Logistic Regression (CLR) and Convolutional Support Vector Machine (CSVM-H) networks.
Dr ArunaDevi Karuppasamy
These methods leverage predefined or externally learned filters, overcoming limitations of back-propagation techniques such as vanishing gradients and heavy data requirements.
Experiments showed that CLR and CSVM-H achieved superior accuracy and faster training times on small datasets from Sultan Qaboos University Hospital (SQUH) and BreaKHis, outperforming traditional models like VggNet-16 and ResNet-50. Both models demonstrated high Area Under Curve (AUC) scores (0.83 and 0.84 on the SQUH dataset), highlighting their efficiency for small, labelled datasets with limited computational resources.
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