Fractal Dimension Estimation for Developing Pathological Brain Detection System Based on Minkowski-Bouligand Method

• Main Authors: Yu-Dong Zhang, Xian-Qing Chen, Tian-Ming Zhan, Zhu-Qing Jiao, Yi Sun, Zhi-Min Chen, Yu Yao, Lan-Ting Fang, Yi-Ding Lv, Shui-Hua Wang
• Format: Article
• Language: English
• Published: IEEE 2016-01-01
• Series: IEEE Access
• Subjects: Minkowski Bouligand dimension; genetic algorithm; artificial bee colony; logistic map; number of hidden neuron; K-fold cross validation
• Online Access: https://ieeexplore.ieee.org/document/7572925/

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.