Breast Cancer Classification Using Breast Ultrasound Images with a Hybrid of Transfer Learning and Bayesian-Optimized Fast Learning Network

Abstract

Background Breast cancer remains the most frequent cancer diagnosed in females, resulting in high mortality rates worldwide. Approximately 2.3 million cases are diagnosed annually. If it is detected at an early stage, the rate of survival is significantly improved; therefore, there is an urgent need for techniques that can be used for its effective diagnosis. Method The study aimed to present a hybrid model for breast cancer classification by employing DenseNet201 as a feature extractor and Bayesian-Optimized Fast Learning Network (FLN) as a classifier. The pre-trained DenseNet201 extracts high-quality features from breast ultrasound images on large datasets, which get classified through an FLN optimized using Bayesian techniques for hyperparameter tuning. Results The model performed well by achieving an accuracy of 96.79%, 94.71% F1 score, 96.81% precision, and 93.48% recall, while the AUC for benign, malignant, and normal cases was found to be 0.96, 0.95, and 0.98, respectively. Cross-entropy loss metrics further validated the model on its robust training and validation. Conclusion There is a great potential that this proposed model could enhance breast cancer diagnosis. This indeed is a reliable and efficient clinical solution for application.