Lightweight Classification Network for Pulmonary Tuberculosis Based on CT Images

Abstract

With the continuous development of medical informatics and digital diagnosis, the classification of tuberculosis cases from computed tomography (CT) images of the lung based on deep learning is an important guiding aid in clinical diagnosis and treatment. Due to its potential application in medical image classification, this task has received extensive research attention. Existing related neural network techniques are still challenging in terms of feature extraction of global contextual information of images and network complexity in achieving image classification. To address these issues, this paper proposes a lightweight medical image classification network based on a combination of Transformer and convolutional neural network (CNN) for the classification of tuberculosis cases from lung CT. The method mainly consists of a fusion of the CNN module and the Transformer module, exploiting the advantages of both in order to accomplish a more accurate classification task. On the one hand, the CNN branch supplements the Transformer branch with basic local feature information in the low level; on the other hand, in the middle and high levels of the model, the CNN branch can also provide the Transformer architecture with different On the other hand, in the middle and high levels of the model, the CNN branches can also provide different local and global feature information to the Transformer architecture to enhance the ability of the model to obtain feature information and improve the accuracy of image classification. A shortcut is used in each module of the network to solve the problem of poor model results due to gradient divergence and to optimise the effectiveness of TB classification. The proposed lightweight model can well solve the problem of long training time in the process of TB classification of lung CT and improve the speed of classification. The proposed method was validated on a computed tomography (CT) image dataset provided by the First Hospital of Lanzhou University. The experimental results show that the proposed lightweight classification network for tuberculosis based on CT medical images of lungs can fully extract the feature information of the input images and obtain high accuracy classification results.