A dimension-reduction based multilayer perception method for supporting the medical decision making

Abstract Due to the rapid development of Medical IoT recently, how to effectively apply these huge amounts of IoT data to enhance the reliability of the clinical decision making has become an increasing issue in the medical field. These data usually comprise high-complicated features with tremendous volume, and it implies that the simple inference models may less powerful to be practiced. In deep learning, multilayer perceptron (MLP) is a kind of feed-forward artificial neural network, and it is one of the high-performance methods about stochastic scheme, fitness approximation, and regression analysis. To process these high uncertain data, the proposed work based on MLP structure in particular integrates the boosting scheme and dimension-reduction process. In this proposed work, the advanced ReLU-based activation function is used. Also, the weight initialization is applied to improve the stable prediction and convergence. After the improved dimension-reduction process is introduced, the proposed method can effectively learn the hidden information from the reformative data and the precise labels also can be recognized by stacking a small amount of neural network layers with paying few extra cost. The proposed work shows a possible path of embedding dimension reduction in deep learning structure with minor price. In addition to the prediction issue, the proposed method can also be applied to assess risk and forecast trend among different information systems.

[1]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Yun Yang,et al.  Hybrid Sampling-Based Clustering Ensemble With Global and Local Constitutions , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[5]  Venkatesan Rajinikanth,et al.  A Hybrid Framework to Evaluate Breast Abnormality Using Infrared Thermal Images , 2019, IEEE Consumer Electronics Magazine.

[6]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[7]  David Minnen,et al.  Full Resolution Image Compression with Recurrent Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Shaowen Yao,et al.  Multi-Sensor Image Fusion Based on Interval Type-2 Fuzzy Sets and Regional Features in Nonsubsampled Shearlet Transform Domain , 2018, IEEE Sensors Journal.

[9]  C. R. Rao,et al.  The Utilization of Multiple Measurements in Problems of Biological Classification , 1948 .

[10]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  Yun Yang,et al.  A robust semi-supervised learning approach via mixture of label information , 2015, Pattern Recognit. Lett..

[13]  Shin-Jye Lee,et al.  A social recommendation method based on the integration of social relationship and product popularity , 2019, Int. J. Hum. Comput. Stud..

[14]  Shaowen Yao,et al.  A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets , 2017, IEEE Access.

[15]  Jianmin Jiang,et al.  Adaptive Bi-Weighting Toward Automatic Initialization and Model Selection for HMM-Based Hybrid Meta-Clustering Ensembles , 2019, IEEE Transactions on Cybernetics.

[16]  Shin-Jye Lee,et al.  Image Classification Based on the Boost Convolutional Neural Network , 2018, IEEE Access.

[17]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[18]  Yun Yang,et al.  A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making , 2017, J. Biomed. Informatics.

[19]  Yun Yang,et al.  An adaptive semi-supervised clustering approach via multiple density-based information , 2017, Neurocomputing.

[20]  Yun Yang,et al.  Bi-weighted ensemble via HMM-based approaches for temporal data clustering , 2018, Pattern Recognit..

[21]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Qian Jiang,et al.  A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition , 2019, Expert Syst. Appl..

[23]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[24]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[25]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[26]  Yun Yang,et al.  Ensemble Learning-Based Person Re-identification with Multiple Feature Representations , 2018, Complex..

[27]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Sarunas Raudys,et al.  Evolution and generalization of a single neurone: I. Single-layer perceptron as seven statistical classifiers , 1998, Neural Networks.

[29]  N. Sri Madhava Raja,et al.  Segmentation of Breast Thermal Images Using Kapur's Entropy and Hidden Markov Random Field , 2017 .

[30]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[31]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[32]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).