Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network

Feng-Ping An

doi:10.1155/2019/9151670

Abstract

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in-depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. In contrast, the proposed method can construct different convolutional neural network models that adapt better to the characteristics of the medical images of interest and thus can better train the resulting heterogeneous multicolumn convolutional neural networks. Finally, using the adaptive sliding window fusion mechanism proposed in this paper, both methods jointly complete the classification task of medical images. Based on the above ideas, this paper proposes a medical classification algorithm based on a weight initialization/sliding window fusion for multilevel convolutional neural networks. The methods proposed in this study were applied to breast mass, brain tumor tissue, and medical image database classification experiments. The results show that the proposed method not only achieves a higher average accuracy than that of traditional machine learning and other deep learning methods but also is more stable and more robust.

Highlights

With the rapid development of computer and medical imaging, medical imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), can noninvasively re ect the physiological state of tissues and organs in the human body. ese techniques have gradually become indispensable tools in medical research, clinical diagnosis, and surgical planning [1,2,3]
The trained heterogeneous, multicolumn convolutional neural networks (CNNs) is combined with an adaptive sliding window fusion mechanism proposed in this paper; this combination completes the medical image classification task
By improving the attracting domain to improve the generalization performance of the solution after convergence, a new method of network weight initialization is proposed. is method alleviates the problem that the initialization theory of existing deep learning is limited by the type, and it increases the potential of the neural network to address different visual tasks

Summary

Introduction

With the rapid development of computer and medical imaging, medical imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), can noninvasively re ect the physiological state of tissues and organs in the human body. ese techniques have gradually become indispensable tools in medical research, clinical diagnosis, and surgical planning [1,2,3]. 2. Deep Learning Model Weight Initialization Method-Based Adaptive Taylor e problem of classifying medical images is essentially one of the identifying features after their extraction. The Microsoft Research Asia (MSRA) method has good convergence and generalizability, but its disadvantage is that it is limited to a specific network type In view of this shortcoming, this section proposes a new initialization method that aims to improve the convergence and generalization ability of the model through optimization techniques. Erefore, the current mainstream initialization method is theoretically not applicable to networks other than those using ReLU or sigmoid activation functions In response to this problem, this paper introduces the Taylor formula and proposes a more general initialization method. The existing initialization method can be considered a special case of the method in this paper

Sliding Window Fusion Method Based on Multilayer Convolutional Neural Network

Experiment Analysis

Method type

Findings

Conclusion