Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.

ABSTRACT: Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best perform

SUBMITTER: Wakili MA 

PROVIDER: S-EPMC9576400 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature