B2 Vertaisarvioimaton kirjan tai muun kokoomateoksen osa
Diagnosis of breast cancer from histopathological images with deep learning architectures
Tekijät: Hancer Emrah, Subasi Abdulhamit
Toimittaja: Subasi Abdulhamit
Kustantaja: Elsevier
Julkaisuvuosi: 2022
Kokoomateoksen nimi: Applications of Artificial Intelligence in Medical Imaging
Tietokannassa oleva lehden nimi: Applications of Artificial Intelligence in Medical Imaging
Sarjan nimi: Artificial Intelligence Applications in Healthcare & Medicine
Aloitussivu: 321
Lopetussivu: 332
ISBN: 978-0-443-18451-2
eISBN: 978-0-443-18450-5
DOI: https://doi.org/10.1016/B978-0-443-18450-5.00002-5
Verkko-osoite: https://www.sciencedirect.com/science/article/abs/pii/B9780443184505000025
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.