Classification of pulmonary tuberculosis lesion with convolutional neural networks

Abstract

The concept of computer-aided diagnosis (CAD) for chest x-rays (CXR) has been around for the past fifty years. CAD can help in early diagnosis and reduce the deaths caused by late diagnosis and lack of treatment. Applying deep learning techniques for classification of medical images has seen considerable growth in recent years. Convolutional Neural Networks (CNNs) are a class of powerful generative models well known for image classification and segmentation. This paper has studied three deep neural networks: AlexNet, VGG-16 and CapsNet, for classifying tuberculosis in CXR images. The customized models are created using the datasets acquired from National Library of Medicine and private Thai datasets. Data augmentation with shuffle sampling is used to prevent overfitting in the constructed models. The performance of classifiers has been evaluated with the measures: accuracy, sensitivity and specificity. All model accuracy increases with the augmented dataset. The method of affine transformation has also applied to investigate the model accuracy when predicting the test set contains variant instances unseen in the training CXR images.