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A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images
Author(s) -
Karthika Gidijala,
Mansa Devi Pappu,
Manasa Vavilapalli,
Mahesh Kothuru
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l9524.10101221
Subject(s) - deep learning , convolutional neural network , artificial intelligence , computer science , convolution (computer science) , architecture , coherence (philosophical gambling strategy) , pattern recognition (psychology) , artificial neural network , machine learning , mathematics , art , statistics , visual arts
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.

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