Published October 30, 2021 | Version v1
Journal article Open

A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

  • 1. Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India
  • 1. Publisher

Description

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.

Files

L952410101221.pdf

Files (449.9 kB)

Name Size Download all
md5:d16c81255b5d6db4b58f5127b283e550
449.9 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2278-3075 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
100.1/ijitee.L952410101221