Abstract

Breast histopathological image analysis for cancer diagnosis using computer tools have gained much attention in the past decade due to the development in computation power. In particular, deep learning-based algorithms which uses deep features are popularly explored for analysing breast histopathological images. However, there exists several challenges in developing computer tools such as heterogeneous characteristic of cancerous cells, illumination variation, color variation etc. Moreover, deep learning models are dependent on large annotated datasets. However, limited benchmark breast histopathological image datasets restricts the application of deep learning models. In this regard, the present paper aims at classification of breast histopathological images at 100x magnification into benign and malignant using deep learning models. Further, this paper demonstrates that data augmentation can improve the accuracy of deep learning models for classification of breast histopathological images. This paper also demonstrates that transferring the features of deep learning models learnt on general object class to and fine tuning it to classify breast histopathological images gives competitive results.

Original languageEnglish
Title of host publicationProceedings of CONECCT 2021
Subtitle of host publication7th IEEE International Conference on Electronics, Computing and Communication Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665428491
DOIs
Publication statusPublished - 2021
Event7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021 - Bangalore, India
Duration: 09-07-202111-07-2021

Publication series

NameProceedings of CONECCT 2021: 7th IEEE International Conference on Electronics, Computing and Communication Technologies

Conference

Conference7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021
Country/TerritoryIndia
CityBangalore
Period09-07-2111-07-21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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