Design of a Telemedicine System for Classification of Breast Cancer Images - Nguyen, Thanh-Tam; Nguyen, Thanh-Hai; Nguyen, Tin-Trung (Journal of Technical Education Science, 2026)
Breast cancer is one of complex breast lesions. Therefore, accurate diagnosis to determine whether there is cancer disease or not, to determine which stage is a challenge for most doctors. This article proposes a telemedicine system for diagnosing breast cancer disease using EfficientNet-B7 in AI model, in which three image sets of Benign, Malignant and Normal are used. The main points are that, this telemedicine system is designed and calculated suitably so that a DICOM image can be transmitted from the image collected place to a server for classification and diagnosis, in which protocols and storage parts in this system are carefully selected and tested for its efficiency. Furthermore, layers and coefficients of the EfficientNet-B7 model are calculated and selected to increase the classification performance. Thus, the overall system results produced an accuracy of about 89.58%, which is a significant result for a complex and challenging system. Thus, the system can be improved in the future by enhancing the image sets, updating the deep learning network appropriately, and configuring a powerful enough server system.