Abstract
Using chest X-ray images is one of the least expensive and easiest ways to diagnose patients who suffer from lung diseases such as pneumonia and bronchitis. Inspired by existing work, a deep learning model is proposed to classify chest X-ray images into 14 lung-related pathological conditions. However, small datasets are not sufficient to train the deep learning model. Two methods were used to tackle this: (1) transfer learning based on two pretrained neural networks, DenseNet and ResNet, was employed; (2) data were preprocessed, including checking data leakage, handling class imbalance, and performing data augmentation, before feeding the neural network. The proposed model was evaluated according to the classification accuracy and receiver operating characteristic (ROC) curves, as well as visualized by class activation maps. DenseNet121 and ResNet50 were used in the simulations, and the results showed that the model trained by DenseNet121 had better accuracy than that trained by ResNet50.
Highlights
Many people suffer from lung diseases such as pneumonia and emphysema every year
Jaiswal et al realized the localization and identification of pneumonia in chest X-ray images using a deep learning model derived from mask-RCNN [9]
Minaee et al applied transfer learning to process chest X-ray images for the detection of COVID-19, and DenseNet121, ResNet18, ResNet50, and SqueezeNet were utilized as the pre-trained networks [12]
Summary
Many people suffer from lung diseases such as pneumonia and emphysema every year. Chest X-ray images are one of the most widely used and low-cost diagnose tools for lung diseases [1]. Many studies have employed transfer learning on small medical datasets and trained neural networks to realize image recognition and classification. Minaee et al applied transfer learning to process chest X-ray images for the detection of COVID-19, and DenseNet121, ResNet, ResNet, and SqueezeNet were utilized as the pre-trained networks [12]. A transfer learning method is proposed to classify 14 lung-related pathologies using frontal-view chest X-ray images. We built image classification models using pretrained networks; We preprocessed the data including data augmentation of the ChestX-ray dataset and dealt with the class imbalance problem; We trained, validated, and tested the model using pretrained networks and compared the performance of each model using the ROC curves.
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