B2 Non-refereed book chapter or chapter in a compilation book

Diagnosis of breast cancer from histopathological images with deep learning architectures




AuthorsHancer Emrah, Subasi Abdulhamit

EditorsSubasi Abdulhamit

PublisherElsevier

Publication year2022

Book title Applications of Artificial Intelligence in Medical Imaging

Journal name in sourceApplications of Artificial Intelligence in Medical Imaging

Series titleArtificial Intelligence Applications in Healthcare & Medicine

First page 321

Last page332

ISBN978-0-443-18451-2

eISBN978-0-443-18450-5

DOIhttps://doi.org/10.1016/B978-0-443-18450-5.00002-5

Web address https://www.sciencedirect.com/science/article/abs/pii/B9780443184505000025


Abstract

Breast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.



Last updated on 2024-26-11 at 22:44