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

Abdullah-Al Nahid; Mohamad Ali Mehrabi; Yinan Kong

doi:10.1109/ISSPIT.2017.8388678

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

Abdullah-Al Nahid, Mohamad Ali Mehrabi, Yinan Kong

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

9 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 language	English
Title of host publication	2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	410-415
Number of pages	6
ISBN (Electronic)	9781538646625
ISBN (Print)	9781538646632
DOIs	https://doi.org/10.1109/ISSPIT.2017.8388678
Publication status	Published - 2017
Event	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain Duration: 18 Dec 2017 → 20 Dec 2017

Conference

Conference	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Country/Territory	Spain
City	Bilbao
Period	18/12/17 → 20/12/17

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10.1109/ISSPIT.2017.8388678

Cite this

Nahid, A.-A., Mehrabi, M. A., & Kong, Y. (2017). Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification. In 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 410-415). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISSPIT.2017.8388678

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title = "Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification",

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.",

author = "Abdullah-Al Nahid and Mehrabi, \{Mohamad Ali\} and Yinan Kong",

year = "2017",

doi = "10.1109/ISSPIT.2017.8388678",

language = "English",

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pages = "410--415",

booktitle = "2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 ; Conference date: 18-12-2017 Through 20-12-2017",

}

Nahid, A-A, Mehrabi, MA & Kong, Y 2017, Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification. in 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 410-415, 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, Bilbao, Spain, 18/12/17. https://doi.org/10.1109/ISSPIT.2017.8388678

Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification. / Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan.
2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 410-415.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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N2 - 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.

AB - 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.

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M3 - Conference proceeding contribution

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BT - 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Y2 - 18 December 2017 through 20 December 2017

ER -

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

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