Deep learning algorithms for breast cancer detection from ultrasound scans

Abstract

Breast cancer is a highly dangerous disease and the leading cause of cancer related deaths among women. Early detection of breast cancer is considered quite challenging but can offer significant benefits, as various treatment interventions can be initiated earlier. The focus of this research is to develop a model to detect breast cancer based on ultrasound results using deep learning algorithms. In the initial stages, several preprocessing processes, including image transformation and image augmentation were performed. Two types of models were developed: utilizing mask files and without using mask files. Two types of models were developed using four deep learning algorithms: residual network (ResNet)-50, VGG16, vision transformer (ViT), and data-efficient image transformer (DeiT). Various algorithms, such as optimization algorithms, loss functions, and hyperparameter tuning algorithms, were employed during the model training process. Accuracy used as the performance metric to measure the model’s effectiveness. The model developed with ResNet-50 became the best model, achieving an accuracy of 94% for the model using mask files. In comparison, the model developed with ResNet-50 and DeiT became the best model for the model without mask files, with an accuracy of 80%. Therefore, it can be concluded that using mask files is crucial for producing the best-performing model.