Affiliation:
1. Purdue University Fort Wayne
Abstract
Abstract
Breast cancer is among one of the most common cancers in the world. Early detection in this area can be crucial and help the patients to start the medication. One way to detect the breast cancer is using histopathology images. In recent years deep learning methods are among the methods that have shown high accuracy in detection cancerous tumors in images. In this work different deep learning methods such as Xception, MobileVNet, VGG16 and VGG19 have been used and the results are compared. Two popular datasets in breast cancer have been used. Transfer learning is used for pre-training the structures. In addition, different preprocessing methods is introduced and used to increase the number of images in the dataset.
Publisher
Research Square Platform LLC
Cited by
1 articles.
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