Researchers from eVida Research Group, University of Deusto, in collaboration with researchers from Bioaraba Health Research Institute, NanoBioCel Research Group (UPV/EHU), BioKeralty Research Institute and Fundación Universitaria Sanitas have published the article entitled “Multiclass classification of breast cancer histopathology images using multilevel features of Deep convolutional neural network” in the Journal Scientific Reports. According to Global Cancer Statistics 2020 (GLOBOCAN 2020), breast cancer is the most common malignancy and the primary cause of cancer-related mortalities in the female population worldwide. Specifically, 2.26 million (11.7% of the total cancer incidence) women were diagnosed, with a mortality of 0.69 million (6.9% of the total cancer deaths) during 2020. Therefore, the premature understanding of breast tumor pathophysiology is crucial, which may help in reducing the morbidity and mortality rates in women worldwide. With this article, researchers seek to improve the quality of diagnosis by introducing a deep learning approach to automatically classify breast cancer microscopic images, which may help to reduce mortality rates in women. DOI: https://doi.org/10.1038/s41598-022-19278-2 Researchers from eVida Research Group (University of Deusto): Zabit Hameed and Begonya Garcia‑Zapirain. Keywords: breast cancer, deep learning, image processing, digital pathology, biomedical engineering. About Scientific Reports Journal Scientific Reports is an open access journal publishing original research from across all areas of the natural sciences, psychology, medicine and engineering. According to Journal Citation Reports Science Edition (Clarivate Analytics, 2022), Scientific Reports is the fifth most-cited journal in the world. Sung, H. et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021), DOI: https://doi.org/10.3322/caac.21660.