Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2916-2923.doi: 10.13229/j.cnki.jdxbgxb20220503

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Enhanced Bagging ensemble learning and multi⁃target detection algorithm

Xiang-jiu CHE(),Ying-jie YU,Quan-le LIU   

  1. College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2022-05-01 Online:2022-12-01 Published:2022-12-08

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Abstract:

Aiming at the problems of low localization accuracy and classification accuracy in medical imaging disease detection by existing target detection algorithms， a multi-target detection method based on dynamic weighted Bagging ensemble learning was proposed. Taking chest imaging disease detection as an example， the Coordinate Attention （CA） module was introduced to enhance the receptive field of the region and improve the weak learner's ability to locate the target region. The dynamic weighted Bagging ensemble learning method was used to give the weak learner voting weight according to the confidence. The variance of the model was reduced， the generalization error was improved， and the classification accuracy was improved. The experimental results show that in the chest imaging disease detection task， the average detection accuracy of the proposed algorithm reaches 41.9%， which is 2.5% higher than that of the YOLOv5 prototype， and G-mean is improved by 1.3%； after the model is weighted and integrated， the average detection accuracy rate reaches 81.06%， which is 1.58% higher than the original model， and has high positioning accuracy and classification accuracy. Therefore， the proposed algorithm can better complete the task of chest imaging disease detection.

Key words: computer application, chest imaging disease detection, dynamically weighted Bagging ensemble learning, coordinate attention

CLC Number: 

  • TP391.4

Fig.1

General structure of ensemble learning"

Fig.2

Detection method framework based on dynamic weighted Bagging integrated learning strategy"

Fig.3

Partial data display"

Table 1

disease label"

病理学标签疾病描述数量
主动脉扩张发生在主动脉血管壁的异常隆起3067
心脏扩大成年病人的心胸比率大于0.52300
实质化肺泡内充满液体、脓液、血液、细胞（包括肿瘤细胞）或其他物质，导致大叶性、弥漫性或多灶性不清混浊的病理过程。386
胸膜增厚涉及壁层或脏层胸膜的任何形式的增厚1981
胸腔积液胸腔内异常积液1032
肺纤维化肺里有多余的纤维组织1617

Fig.4

Data augmentation"

Fig.5

Detection frame size"

Fig.6

CA model"

Fig.7

Proportion of category distribution"

Fig.8

Dynamic weight Bagging ensemble learning"

Table 2

Side effects of ensemble learning"

模型测试例1测试例2测试例3
1√ⅹⅹ
2ⅹ√ⅹ
3ⅹⅹ√
集成ⅹⅹⅹ

Table 3

Confusion matrix"

真实情况预测结果
正例反例
正例TPFN
反例FPTN

Table 4

Comparison of recall performance of of different weak learners"

方法主动脉扩张心脏扩大实质化胸膜增厚胸腔积液肺纤维化均值
Faster-RCNN0.5210.8300.5370.4340.4430.3540.520
YOLOv40.6320.7320.6040.5110.4820.3950.559
YOLOv50.6510.8000.6350.5320.5000.4520.595
CA-YOLOv50.6620.8280.6410.5380.5240.4640.610

Table 5

Comparison of mAP values of different weak learners"

方法主动脉扩张心脏扩大实质化胸膜增厚胸腔积液肺纤维化均值
Faster-RCNN0.5000.5450.2020.1850.2830.1600.313
YOLOv40.6570.5250.2490.2090.3490.1900.363
YOLOv50.6720.5390.2630.2220.4020.2650.394
CA-YOLOv50.6990.5500.3230.2350.4280.2780.419

Table 6

G-mean value comparison of different weak learners"

方法主动脉扩张心脏扩大实质化胸膜增厚胸腔积液肺纤维化均值
Faster-RCNN0.4630.5900.3750.4890.5000.4200.473
YOLOv40.5400.5810.3920.5240.5340.4610.505
YOLOv50.5890.5890.4030.5680.5380.4890.529
CA-YOLOv50.6020.5910.4420.5720.5430.5020.542

Table 7

Accuracy comparison before and after model integration"

方法主动脉扩张心脏扩大实质化胸膜增厚胸腔积液肺纤维化均值
CA-YOLOv584.7689.7878.1376.9371.3475.9279.48
+集成模型85.3890.6579.0378.5172.6277.7380.65
+动态权重85.5091.0379.8078.9172.8878.2381.06

Fig.9

Comparison of actual detection effect diagrams"

Fig.10

Attention heatmap comparison"

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