Deep Learning in Automating Breast Cancer Diagnosis from Microscopy Images

Context Breast cancer is one of the most common cancers in women. With early diagnosis, some breast cancers are highly curable. However, the concordance rate of breast cancer diagnosis from histology slides by pathologists is unacceptably low. Classifying normal versus tumor breast tissues from microscopy images of breast histology is an ideal case to use for deep learning and could help to more reproducibly diagnose breast cancer. Since data preprocessing and hyperparameter configurations have impacts on breast cancer classification accuracies of deep learning models, training a deep learning classifier with appropriate data preprocessing approaches and optimized hyperparameter configurations could improve breast cancer classification accuracy. Methods and Material Using 12 combinations of deep learning model architectures (i.e., including 5 non-specialized and 7 digital pathology-specialized model architectures), image data preprocessing, and hyperparameter configurations, the validation accuracy of tumor versus normal classification were calculated using the B re A st C ancer H istology (BACH) dataset. Results The DenseNet201, a non-specialized model architecture, with transfer learning approach achieved 98.61% validation accuracy compared to only 64.00% for the digital pathology-specialized model architecture. Conclusions The combination of image data preprocessing approaches and hyperparameter configurations have a profound impact on the performance of deep neural networks for image classification. To identify a well-performing deep neural network to classify tumor versus normal breast histology, researchers should not only focus on developing new models specifically for digital pathology, since hyperparameter tuning for existing deep neural networks in the computer vision field could also achieve a high (often better) prediction accuracy. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the University of Minnesota Graduate School Doctoral Dissertation Fellowship for the year of 2022-2023, and the Department of Laboratory Medicine and Pathology at the Mayo Clinic. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors