Breast Cancer Histopathological Image Classification using Progressive Resizing Approach
Published in The 1st International Conference on Emerging Issues in Technology, Engineering, and Science (ICE-TES), 2021
Breast cancer (BC) is a lethal disease which causes the second largest number of deaths among women in the world. A diagnosis of biopsy tissue stained with hematoxylin & eosin, commonly named BC histopathological image, is a non-trivial task which requires a specialist to interpret. Recently, the advance in machine learning techniques driven by deep learning techniques and competition datasets has enabled the automation and prediction of histopathological images interpretation. Each different competition dataset has its own state-of-the-art technique; therefore, this paper explores an avenue of research by merging popular BC histopathological images research datasets and searching for the best performing models on the unified dataset.