Classification of 2D Ultrasound Breast Cancer Images with Deep Learning

Jack Ellis, Kofi Appiah, Emmanuel Amankwaa-Frempong, Sze Chai Kwok; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5167-5173

Breast cancer is the second most prevalent form of cancer and is the "leading cause of most cancer-related deaths in women". Most women living in low- and middle-income countries (LMIC) have limited access to the existing poor health systems restricted access to treatment facilities and in general lack of breast cancer screening programmes. The likelihood of women living in LMIC attending a health facility with advanced-stage breast cancer is very high and the chances of them being able to afford treatment at that stage even if the treatment is available is very low. In this work we evaluate the capabilities of deep learning as a classification tool with the aim of detecting cancerous ultrasound breast images. We aim to deploy a simple classifier on a mobile device with an inexpensive handheld ultrasound imaging system to pick up breast cancer cases that will need medical attention. We demonstrate in this work that with minimal ultrasound images a de novo system trained from scratch can achieve accuracy of close to 64% and about 78% when the same model is pre-trained.