Affiliations: [a]
Department of Ultrasound, Sunshine Union Hospital, Weifang, China
| [b]
College of Health, Binzhou Polytechnical College, Binzhou, China
| [c]
Department of Imaging, Qingdao Hospital of Traditional Chinese Medicine (Qingdao HaiCi Hospital), Qingdao, China
Correspondence:
[*]
Corresponding author: Jingde Zhao, Department of Imaging, Qingdao Hospital of Traditional Chinese Medicine (Qingdao HaiCi hospital), No.4, Renmin Road, Shibei District, Qingdao City, 266033, China. E-mail: zjdhiser_ys@sina.com.
Note: [1] This paper is published in the Special Issue of Medical Applications of X-Ray Imaging Techniques: Advances and Innovations. The Guest Editors are Dr. Amita Nandal (nandalamita6@gmail.com) and Dr. Malik Bader Alazzam (m.alazzam@aau.edu.jo).
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.
Keywords: Breast cancer, ultrasound imaging, deep learning, Convoluted Neural Networks, image classification
