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Extracting Time-Frequency Features of Images for Robust LSTM-based Classification of H&E Stained Tissue

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posted on 08.12.2020, 14:20 by Tuan Pham
The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

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Email Address of Submitting Author

tdpham123@gmail.com

ORCID of Submitting Author

0000-0002-4255-5130

Submitting Author's Institution

Prince Mohammad Bin Fahd University

Submitting Author's Country

Saudi Arabia

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