期刊
IEEE ACCESS
卷 4, 期 -, 页码 5937-5947出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2611530
关键词
Minkowski Bouligand dimension; genetic algorithm; artificial bee colony; logistic map; number of hidden neuron; K-fold cross validation
资金
- NSFC [61602250, 61502206]
- Natural Science Foundation of Jiangsu Province [BK20150983, BK20150523]
- Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology [2016WLZC013]
- Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing [BD201607]
It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of hidden neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.
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