Learning technology for detection and grading of cancer tissue using tumour ultrasound images1

Abstract

BACKGROUND: Early diagnosis of breast cancer is crucial to perform effective therapy. Many medical imaging modalities including MRI, CT, and ultrasound are used to diagnose cancer. OBJECTIVE: This study aims to investigate feasibility of applying transfer learning techniques to train convoluted neural networks (CNNs) to automatically diagnose breast cancer via ultrasound images. METHODS: Transfer learning techniques helped CNNs recognise breast cancer in ultrasound images. Each model’s training and validation accuracies were assessed using the ultrasound image dataset. Ultrasound images educated and tested the models. RESULTS: MobileNet had the greatest accuracy during training and DenseNet121 during validation. Transfer learning algorithms can detect breast cancer in ultrasound images. CONCLUSIONS: Based on the results, transfer learning models may be useful for automated breast cancer diagnosis in ultrasound images. However, only a trained medical professional should diagnose cancer, and computational approaches should only be used to help make quick decisions.