Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification

Abdullah-Al Nahid, Mohamad Ali Mehrabi, Yinan Kong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

Biomedical image classification has always been a challenging and critical task which has the highest level of importance. The Deep Neural Network (DNN) has been recently introduced for normal image classification and lately introduced for Biomedical image classification with some advanced engineering. In this paper we have classified an image dataset with a DNN utilizing Long Short Term Memory (LSTM) as well as Gated Recurrent Unit (GRU) for breast-image classification. Instead of directly using raw images, we have utilized frequency-domain information for the image classification. Using our model we have obtained 93.01% Accuracy, 94.00% Recall and 94.00% Precision, which is the best available result on this dataset.

Original languageEnglish
Title of host publication2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages410-415
Number of pages6
ISBN (Electronic)9781538646625
ISBN (Print)9781538646632
DOIs
Publication statusPublished - 2017
Event17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain
Duration: 18 Dec 201720 Dec 2017

Conference

Conference17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
CountrySpain
CityBilbao
Period18/12/1720/12/17

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