The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images
|
|
Author(s): Anu Singha (Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India)and Jayanthi Ganapathy (Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India)
Copyright: 2022
Pages: 22
Source title:
AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
Source Author(s)/Editor(s): Sandeep Kumar Kautish (Chandigarh University, Mohali, India)and Gaurav Dhiman (Government Bikram College of Commerce, India & Lebanese American University, Lebanon)
DOI: 10.4018/978-1-6684-4405-4.ch002
Purchase
|
Abstract
Due to the complexity of histopathology tissues, an accurate classification and segmentation of cancer diagnosis is a challenging task in computer vision. The nuclei segmentation of microscopic images is a key prerequisite for cancerous pathological image analysis. However, an accurate nuclei segmentation is a long running major challenge due to the enormous color variability of staining, nuclei shapes, sizes, and clustering of overlapping cells. To address these challenges and early diagnosis as well as reduce the bias decisions of expert lab technician of cancer in clinical practice, the authors study the classification of computer-aided frameworks and automatic nuclei segmentation frameworks based on histopathology images by convolutional deep learning. The authors have used a publicly available PatchCamelyon and 2018 Data Science Bowl histology image dataset for this study. The results are compared and expected to be useful clinically for technician experts in the analysis of cancer diagnosis and the survival chances of patients.
Related Content
|
Mohammed Adi Al Battashi, Mohamad A. M. Adnan, Asyraf Isyraqi Bin Jamil, Majid Adi Al-Battashi.
© 2026.
30 pages.
|
|
Potchong M. Jackaria, Al-adzran G. Sali, Hana An L. Alvarado, Rashidin H. Moh. Jiripa, Al-sabrie Y. Sahijuan.
© 2026.
26 pages.
|
|
Elizabeth Gross.
© 2026.
30 pages.
|
|
Siti Nazleen Abdul Rabu, Xie Fengli, Ng Man Yi.
© 2026.
44 pages.
|
|
Mohammed Abdul Wajeed.
© 2026.
30 pages.
|
|
Aldammien A. Sukarno, Al-adzkhan N. Abdulbarie, Wati Sheena M. Bulkia, Potchong M. Jackaria.
© 2026.
24 pages.
|
|
Abdulla Sultan Binhareb Almheiri, Humaid Albastaki, Hanadi Alrashdan.
© 2026.
26 pages.
|
|
|