Osprey optimization algorithm for VGG16 hyperparameter optimization in breast cancer detection

Abstract

Globally, breast cancer is one of the reason for mortality among women and accurate automated diagnosis remains a critical research challenge. This research is used to improve breast cancer classification performance by optimizing deep learning (DL) model hyperparameters using a bio-inspired optimization technique. The osprey optimization algorithm (OOA) is applied to fine-tune the hyperparameters of the VGG16 convolutional neural network (CNN) for histopathological breast cancer image classification. The optimized model is evaluated using a curated dataset and compared with established DL architectures, including AlexNet, Xception, InceptionV3, and ResNet50. Performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, specificity, AUC-ROC, Matthews correlation coefficient (MCC), log loss, and inference time. Experimental results indicate that the OOA-optimized VGG16 model achieves superior performance, with an accuracy of 97.7%, precision of 96.71%, recall of 97.79%, AUC-ROC of 99.92%, and MCC of 0.9449, while maintaining competitive computational efficiency. The results demonstrate that bio-inspired hyperparameter optimization significantly enhances classification reliability and diagnostic accuracy. In summary, integrating OOA optimization with the VGG16 architecture yields a dependable framework for breast cancer identification, making it a promising candidate for deployment in automated diagnostic support systems.