Welcome to the InfoSci Platform
A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images

A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images

Anu Singha, Jayanthi Ganapathy
Copyright: © 2022 |Pages: 22
ISBN13: 9781668444054|ISBN10: 1668444054|ISBN13 Softcover: 9781668444061|EISBN13: 9781668444078
DOI: 10.4018/978-1-6684-4405-4.ch002
Cite Chapter Cite Chapter

MLA

Singha, Anu, and Jayanthi Ganapathy. "A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images." AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management, edited by Sandeep Kumar Kautish and Gaurav Dhiman, IGI Global, 2022, pp. 26-47. https://doi.org/10.4018/978-1-6684-4405-4.ch002

APA

Singha, A. & Ganapathy, J. (2022). A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images. In S. Kautish & G. Dhiman (Eds.), AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management (pp. 26-47). IGI Global. https://doi.org/10.4018/978-1-6684-4405-4.ch002

Chicago

Singha, Anu, and Jayanthi Ganapathy. "A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images." In AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management, edited by Sandeep Kumar Kautish and Gaurav Dhiman, 26-47. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-4405-4.ch002

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.