Digital Repository

A Systematic Approach to Identify the Breast Cancer Grades in Histopathological Images Using Deep Neural Networks

Show simple item record

dc.contributor.author Silva, S.H.S.
dc.contributor.author Jinesena, T. M. K. K.
dc.date.accessioned 2022-02-25T03:45:45Z
dc.date.available 2022-02-25T03:45:45Z
dc.date.issued 2021
dc.identifier.citation Silva S.H.S., Jinesena T. M. K. K. (2021), A Systematic Approach to Identify the Breast Cancer Grades in Histopathological Images Using Deep Neural Networks, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 20-25 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/24489
dc.description.abstract Breast cancer can be recognized as one of the most well-known and life-threatening cancers impacting women and this has been identified as the second most common cancer across the world. According to registered data, there were over 2 million newly reported cases in 2020. The Deep Convolutional Neural Network has been identified as one of the most dominant and powerful deep learning approaches involved in the analysis of visual imagination. There are many shreds of evidence that indicate the appropriateness of this in medical imaging including breast cancer detection, lassification, and segmentation with higher accuracy rates. The main intent of the research is to develop an automated application that can determine the Nottingham Histologic Score of a given input histopathological image obtained from breast cancer or healthy tissues with DenseNet based architecture. Healthy or benign tissues are categorized as zero and cancerous tissues are categorized based on the grade obtained as one, two, or three. In this study, we were able to obtain more than 94% accuracy rates for each trained model including 2-predict, 3-predict, and 4-predict networks. Further, a desktop-based inference tool that allows us to perform breast cancer grading was also developed as a result of this study. en_US
dc.publisher Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka en_US
dc.subject Breast Cancer Grading, Computer-Aided Diagnosis, DenseNet, Histopathological Images,Nottingham Histologic Score en_US
dc.title A Systematic Approach to Identify the Breast Cancer Grades in Histopathological Images Using Deep Neural Networks en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Digital Repository


Browse

My Account