Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Fengping An,Xiaowei Li,Xingmin Ma

doi:10.1155/2021/6280690

Abstract

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

Highlights

Medical imaging is a technique and process for noninvasively obtaining a part of a tissue image of a human body
The medical image classification algorithm proposed in this paper and other mainstream medical image classification algorithms were used to classify the lung nodule database established by Japanese Society of Radiology Technology (JSRT)
The recognition accuracy of the deep learning method proposed in the literature [52,53,54] reached 98.53%, 99.02%, and 99.23%, respectively. Their classification accuracy rate was over 98%, which was more than 3% higher than traditional machine learning methods because the deep learning model obtained a more reasonable and reliable image classification model for Wisconsin Breast Cancer Database (WBCD) database training

Summary

Introduction

Medical imaging is a technique and process for noninvasively obtaining a part of a tissue image of a human body. Differences in the clinical experience of doctors may result in a decline in the quality of medical image analysis and may even lead to misdiagnosis and missed diagnosis of patients [1, 2]. A reliable computer-aided diagnostic system can improve the above situation. Improving the performance of medical imaging film computer-aided diagnosis systems has become an important issue that has attracted the attention of relevant scholars [1]. Image classification technology can carry out preliminary analysis and understanding of medical images and can effectively identify the corresponding lesion areas, assisting doctors in pathological diagnosis [2, 3]. There are many current image classification methods, they can be generally divided into image classification methods based on traditional machine learning and image classification methods based on deep learning [4,5,6]

Methods

Results

Conclusion