Disease Classification Algorithm of Chest X-Ray Based on Efficient Channel Attention

Fig. 6. X-ray images in Chest X-ray 15 dataset. (a) No finding; (b) pneumonia;(c) COVID-19; (d) cardiomegaly; (e) hernia; (f) infiltration;(g) nodule; (h) emphysema; (i) effusion; (j) pleural thickening; (k) pneumothorax; (l) mass; (m) fibrosis; (n) edema; (o) consolidation

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Fig. 7. ROC curve and AUC value of proposed algorithm. (a) Atelectasis; (b) cardiomegaly; (c) effusion; (d) infiltration; (e) mass; (f) nodule; (g) pneumonia; (h) pneumothorax; (i) consolidation; (j) edema; (k) emphysema; (l) fibrosis; (m) pleural thickening: (n) hernia; (o) COVID-19

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Table 1. Specific structure of DECA-Net

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Table 1. Specific structure of DECA-Net

Layer	Output size	DECANet-121
Convolution	112×112	7×7 Conv，stride 2
Pooling	56×56	3×3 max pool，stride 2
DECA block 1	56×56	$\{\begin{array}{l} G A P \\ 1 \times 1 C o n v \times k \\ S i g m o i d \\ 1 \times 1 C o n v \\ 3 \times 3 A C B \end{array}\} \times 6$
Transition layer 1	56×56	1×1 Conv
Transition layer 1	28×28	2×2 average pool，stride 2
DECA block 2	28×28	$\{\begin{array}{l} G A P \\ 1 \times 1 C o n v \times k \\ S i g m o i d \\ 1 \times 1 C o n v \\ 3 \times 3 A C B \end{array}\} \times 12$
Transition layer 2	28×28	1×1 Conv
Transition layer 2	14×14	2×2 average pool，stride 2
DECA block 3	14×14	$\{\begin{array}{l} G A P \\ 1 \times 1 C o n v \times k \\ S i g m o i d \\ 1 \times 1 C o n v \\ 3 \times 3 A C B \end{array}\} \times 24$
Transition layer 3	14×14	1×1 Conv
Transition layer 3	7×7	2×2 average pool，stride 2
DECA block 4	7×7	$\{\begin{array}{l} G A P \\ 1 \times 1 C o n v \times k \\ S i g m o i d \\ 1 \times 1 C o n v \\ 3 \times 3 A C B \end{array}\} \times 16$
Classification layer	1×1	7×7 global average pool，stride 2
Classification layer		15 fully-connected

Table 2. Comparison of classification results of different classification network models on Chest X-ray 15
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Table 2. Comparison of classification results of different classification network models on Chest X-ray 15
Network Backbone Average AUC
ResNet50 ResNet50 0.7468
ResNet50+SE ResNet50 0.7642
ResNet50+ECA DenseNet121
ResNet50
DenseNet121
0.7886
0.7952
DenseNet121+SE^［10］ DenseNet121 0.8014
DECA-Net DenseNet121 0.8245

Table 3. Comparison of AUC value of different chest disease classification algorithms on Chest X-ray 14 dataset

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Table 3. Comparison of AUC value of different chest disease classification algorithms on Chest X-ray 14 dataset

Disease	Algorithm of reference［9］	Algorithm of reference［10］	Algorithm of reference［13］	Algorithm of reference［18］	Algorithm of reference［19］	Algorithm of reference［20］	Algorithm of reference［21］	Proposedalgorithm
Atelectasis	0.7003	0.7627	0.785	0.767	0.783	0.791	0.785	0.8157
Cardiomegaly	0.81	0.8835	0.8766	0.883	0.884	0.898	0.887	0.8657
Effusion	0.7585	0.8159	0.8628	0.828	0.832	0.873	0.831	0.8701
Infiltration	0.6614	0.6786	0.673	0.709	0.708	0.700	0.703	0.6948
Mass	0.6933	0.8012	0.804	0.821	0.837	0.832	0.833	0.8350
Nodule	0.6687	0.7293	0.7299	0.758	0.800	0.758	0.798	0.7683
Pneumonia	0.658	0.7097	0.7423	0.731	0.735	0.767	0.731	0.7548
Pneumothorax	0.7993	0.8377	0.8426	0.846	0.866	0.859	0.881	0.8687
Consolidation	0.7032	0.7443	0.7846	0.745	0.746	0.800	0.754	0.7952
Edema	0.8052	0.8414	0.8727	0.835	0.841	0.889	0.849	0.8647
Emphysema	0.833	0.8836	0.858	0.895	0.937	0.891	0.930	0.8942
Fibrosis	0.7859	0.8007	0.7754	0.818	0.82	0.789	0.833	0.8141
Pleural thickening	0.6835	0.7536	0.7563	0.761	0.796	0.771	0.782	0.7872
Hernia	0.8717	0.8763	0.8645	0.896	0.895	0.896	0.921	0.9027
Mean	0.7451	0.7941	0.802	0.807	0.82	0.822	0.823	0.8237

Table 4. Comparison of ablation experiment results

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Table 4. Comparison of ablation experiment results

Disease	Network_1	Network_2	Network_3	Network_4	DECA-Net
Atelectasis	0.7853	0.7838	0.7974	0.8073	0.8101
Cardiomegaly	0.8770	0.8702	0.8813	0.8877	0.8919
Effusion	0.8541	0.8498	0.8614	0.8689	0.8701
Infiltration	0.6696	0.6724	0.6845	0.6946	0.6892
Mass	0.8128	0.8090	0.8306	0.8302	0.8301
Nodule	0.739	0.7244	0.7595	0.7534	0.7672
Pneumonia	0.7282	0.7355	0.7450	0.7412	0.7389
Pneumothorax	0.8546	0.8623	0.8743	0.8806	0.8829
Consolidation	0.7818	0.7849	0.7908	0.7975	0.7931
Edema	0.8633	0.8872	0.8726	0.8805	0.8782
Emphysema	0.8742	0.8829	0.8924	0.9023	0.9122
Fibrosis	0.7722	0.7892	0.7859	0.796	0.8054
Pleural thickening	0.7607	0.7788	0.7627	0.7707	0.7851
Hernia	0.8571	0.9011	0.9004	0.8678	0.8893
COVID-19	0.8265	0.8157	0.8273	0.8357	0.8239
Mean	0. 8038	0.8098	0.8177	0.8210	0.8245

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Lingyun Shao, Qiang Li, Xin Guan, Xuewen Ding. Disease Classification Algorithm of Chest X-Ray Based on Efficient Channel Attention[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1217001

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Paper Information

Category: Medical Optics and Biotechnology

Received: Feb. 17, 2022

Accepted: May. 25, 2022

Published Online: Jun. 5, 2023

The Author Email: Xin Guan (guanxin@tju.edu.cn)

DOI:10.3788/LOP220759

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Specific structure of DECA-Net

Table 1. Specific structure of DECA-Net

Table 2. Comparison of classification results of different classification network models on Chest X-ray 15

Table 2. Comparison of classification results of different classification network models on Chest X-ray 15

Table 3. Comparison of AUC value of different chest disease classification algorithms on Chest X-ray 14 dataset

Table 3. Comparison of AUC value of different chest disease classification algorithms on Chest X-ray 14 dataset

Table 4. Comparison of ablation experiment results

Table 4. Comparison of ablation experiment results