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Biotechnology & Biotechnological Equipment Volume 34, 2020 - Issue 1
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Research Article

A blood cell dataset for lymphoma classification using faster R-CNN

Biaosheng Shenga Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Mei Zhoua Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Menghan Hua Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Qingli Lia Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR China;b Engineering Center of SHMEC for Space Information and GNSS, Shanghai, PR ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-5063-8801View further author information
ORCID Icon,
Li Suna Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
&
Ying Wena Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
Pages 413-420 | Received 04 Oct 2019, Accepted 04 May 2020, Published online: 13 May 2020

Figures & data

Figure 1. One target cell per image (a) and multiple target cells per image (b).

Figure 1. One target cell per image (a) and multiple target cells per image (b).

Figure 2. Lymphoma (a), blast (b), lymphocyte (c) and other cell (d).

Figure 2. Lymphoma (a), blast (b), lymphocyte (c) and other cell (d).

Table 1. Total number of cells of each class in the dataset.

Figure 3. Architecture of Faster R-CNN.

Figure 3. Architecture of Faster R-CNN.

Table 2. Number of images of each part.

Table 3. mAP of 6 combinations of different methods and networks.

Figure 4. Loss for ZF (red), VGG16 (green), and VGG_CNN_M_1024 (blue).

Figure 4. Loss for ZF (red), VGG16 (green), and VGG_CNN_M_1024 (blue).

Table 4. Confusion matrix of the final model.

Table 5. Confusion matrix of KNN.

Table 6. Confusion matrix of VGG16.

Data availability statement

Raw data were generated at East China Normal University. Derived data supporting the findings of this study are available from the corresponding author Qingli Li on request. More details can be found from http://bio-hsi.ecnu.edu.cn

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