NHL pathological image classification based on hierarchical local information and GoogLeNet-based representations

Bai, J., Jiang, H., Li, S. and Ma, X. ORCID: 0000-0003-0074-4192, 2019. NHL pathological image classification based on hierarchical local information and GoogLeNet-based representations. BioMed Research International, 2019: 1065652. ISSN 2314-6133

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Abstract

Background.

Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL).

Methods.

There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result.

Results.

The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998.

Conclusion.

The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.

Item Type:	Journal article
Publication Title:	BioMed Research International
Creators:	Bai, J., Jiang, H., Li, S. and Ma, X.
Publisher:	Hindawi Publishing Corporation
Date:	21 March 2019
Volume:	2019
ISSN:	2314-6133
Identifiers:	Number Type 10.1155/2019/1065652 DOI 1065652 Publisher Item Identifier
Rights:	Copyright © 2019 Jie Bai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	01 Jul 2019 15:19
Last Modified:	01 Jul 2019 15:19
URI:	https://irep.ntu.ac.uk/id/eprint/36990

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