Deep learning-based pneumonia detection from chest X-ray images using a convolutional neural network

目的 基于计算机的胸腔X线影像疾病检测和分类目前存在误诊率高，准确率低的问题。本文在视觉Transformer（vision Transformer，ViT）预训练模型的基础上，通过迁移学习方法，实现胸腔X线影像辅助诊断，提高诊断准确率和效率。方法 选用带有卷积神经网络（convolutional neural network，CNN）的ViT模型，其在超大规模自然图像数据集中进行了预训练；通过微调模型结构，使用预训练的ViT模型参数初始化主干网络，并迁移至胸腔X线影像数据集中再次训练，实现疾病多标签分类。结果 在IU X-Ray数据集中对ViT迁移学习前、后模型平均AUC（area under ROC curve）得分进行对比分析实验。结果表明，预训练ViT模型平均AUC得分为0.774，与不使用迁移学习相比提升了0.208。并针对模型结构和数据预处理进行了消融实验，对ViT中的注意力机制进行可视化，进一步验证了模型有效性。最后使用Chest X-Ray14和CheXpert数据集训练微调后的ViT模型，平均AUC得分为0.839和0.806，与对比方法相比分别有0.014～0.031的提升。结论 与其他方法相比，ViT模型胸腔X线影像的多标签分类精确度更高，且迁移学习可以在降低训练成本的同时提升ViT模型的分类性能和泛化性。消融实验与模型可视化表明，包含CNN结构的ViT模型能重点关注有意义的区域，高效获取胸腔X线影像的视觉特征。;Objective The chest X-ray-relevant screening and diagnostic method is essential for radiology nowadays. Most of chest X-ray images interpretation is still restricted by clinical experience and challenged for misdiagnose and missed diagnoses. To detect and identify one or more potential diseases in images automatically，it is beneficial for improving diagnostic efficiency and accuracy using computer-based technique. Compared to natural images，multiple lesions are challenged to be detected and distinguished accurately in a single image because abnormal areas have a small proportion and complex representations in chest X-ray images. Current convolutional neural network（CNN）based deep learning models have been widely used in the context of medical imaging. The structure of the CNN convolution kernel has sensitive to local detail information，and it is possible to extract richer image features. However，the convolution kernel cannot be used to get global information，and the features-extracted are restricted of redundant information like its relevance of background， muscles，and bones. The model’s performance in multi-label classification tasks are affected to a certain extent. At present，the vision Transformer（ViT）model has achieved its priorities in computer vision-related tasks. The ViT can be used to capture information simultaneously and effectively for multiple regions of the entire image. However，it is required to use large-scale dataset training to achieve good performance. Due to some factors like patient privacy and manual annotate costs，the size of the chest X-ray image data set has been limited. To reduce the model's dependence on data scale and improve the performance of multi-label classification，we develop the CNN-based ViT pre-training model in terms of the transfer learning method for diagnosis-assisted of chest X-ray image and multi-label classification. Method The CNN-based ViT model is pre-trained on a huge scale ground truth dataset，and it is used to obtain the initial parameters of the model. The model structure is fine-tuned according to the features of chest X-ray dataset. A 1×1 convolution layer is used to convert the chest X-ray images channels between 1 to 3. The number of output nodes of the linear layer in the classifier is balanced from 1 000 to the number of chest X-ray classification labels，and the Sigmoid is used as an activation function. The parameters of the backbone network are initialized in terms of the pre-trained ViT model parameters，and it is trained in the chest X-ray dataset after that to complete multi-label classification. The experiment is configured of Python3. 7 and PyTorch1. 8 to construct the model and RTX3090 GPU for training. Stochastic gradient descent（SGD）optimizer，binary cross-entropy（BCE）loss function，an initial learning rate of 1E-3，the cosine annealing learning rate decay are used. For training，each image is scaled to a size of 512×512 pixels，and a 224×224 pixels area and it is then cropped in random as the model input，and data augmentation is performed randomly by some of the flipping，perspective transformation， shearing，translation，zooming，and changing brightness. For testing，the chest X-ray image is scaled to 256×256 pixels and center crop a 224×224 area to input the trained model. Result The experiment is performed on the IU X-Ray，which is a small-scale chest X-ray dataset. This model is evaluated in quantitative using the average of area under ROC curve （AUC）scores across all classification labels. The results show that the average AUC score of the pre-trained ViT model is 0. 774. The accuracy and training efficiency of the non-pre-trained ViT model is dropped significantly. The average AUC score is reached to 0. 566 only，which is 0. 208 lower. In addition，the attention mechanism heat map is generated based on the ViT model，which can strengthen the interpretability of the model. A series of ablation experiments are carried out for data augmentation，model structure，and batch size design. The fine-tuned ViT model is trained on the Chest-Ray14 and CheXpert dataset as well. The average AUC score is reached to 0. 839 and 0. 806，which is optimized by 0. 014 and 0. 031. Conclusion A pre-trained ViT model is used for the multi-label classification of chest X-ray images via transfer learning. The experimental results illustrate that the ViT has its stronger multi-label classification performance in chest Xray images，and its attention mechanism is beneficial for lesions precision-focused like the interior of the chest cavity and the heart. Transfer learning is potential to improve the classification performance and model generalization of the ViT in small-scale datasets，and the training cost is reduced greatly. Ablation experiments demonstrate that the incorporated model of CNN and Transformer has its priority beyond single-structure model. Data enhancement and the batch size cutting can improve the performance of the model，but smaller scale of batch is still interlinked to longer training span. To improve the model's ability，we predict that future research direction can be focused on the extraction for complex disease and highlevel semantic information，such as their small lesions，disease location，and severity.

BackgroundThis study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor’s point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities.MethodsThe model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN’s region of interest, we applied it to evaluation of the proposed model.ResultsOperation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images.ConclusionsThe proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method

Lung diseases such as COVID-19, pneumonia, and tuberculosis remain major public health challenges worldwide, emphasizing the urgent demand for accurate and efficient diagnostic methods. This research explores the use of a Convolutional Neural Network (CNN)-based framework for binary classification of chest X-ray images to detect abnormalities. The methodology incorporates preprocessing techniques such as image resizing, normalization, data augmentation, and grayscale transformation to improve input data quality. CNN architecture comprising convolutional, pooling, fully connected, and dropout layers were trained and evaluated on publicly available datasets. The model attained a test accuracy of 92%; nevertheless, performance metrics revealed a disparity between the two classified categories. Class 0 (Normal) had precision (83%) and recall (90%), resulting in an F1-score of 0.80, whereas Class 1 (Abnormal) demonstrated higher precision (88%) and recall (90%) with an F1-score of 0.88. This highlights the need for further optimization to enhance the detection of normal cases. The findings underscore the potential of CNNs in automating lung disease detection but also reveal areas for improvement in model robustness and class balance.

In this presentation, we quantify the uncertainty of deep neural networks (DNNs) for the task of Chest X-Ray (CXR) image classification. We investigate uncertainties of several commonly used DNN architectures including ResNet, ResNeXt, DenseNet and SENet. We propose an uncertainty based strategy and analyze the impact of this strategy on the classifier performance. Results show that utilizing uncertainty information may improve DNN performance for some metrics and observations.

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.

Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represented as the graph-structured data. However, most state-of-the-art works only focus on regression from the input to the binary labels, failing to make full use of such valuable graph-structured information due to the complexity of graph data. In this paper, we propose a novel label co-occurrence learning framework based on Graph Convolution Networks (GCNs) to explicitly explore the dependencies between pathologies for the multi-label chest X-ray (CXR) image classification task, which we term the "CheXGCN". Specifically, the proposed CheXGCN consists of two modules, i.e., the image feature embedding (IFE) module and label co-occurrence learning (LCL) module. Thanks to the LCL model, the relationship between pathologies is generalized into a set of classifier scores by introducing the word embedding of pathologies and multi-layer graph information propagation. During end-to-end training, it can be flexibly integrated into the IFE module and then adaptively recalibrate multi-label outputs with these scores. Extensive experiments on the ChestX-Ray14 and CheXpert datasets have demonstrated the effectiveness of CheXGCN as compared with the state-of-the-art baselines.

Chest X-ray images are an essential diagnostic tool in the medical field, but transmitting and storing these images can raise security concerns. Privacy-preserving techniques in healthcare databases protect sensitive patient information from unauthorized access, breaches, and misuse. Using a combination of elliptic curve cryptography (ECC) for encryption, a convolutional neural network (CNN) for feature extraction, and a long short-term memory (LSTM) network for classification, the aim of this research is to develop a safe and effective classification system for chest X-ray images. The research integrates hyperparameter optimization, essential for LSTM, as it dramatically impacts the model's performance. Additionally, the research employs an oppositionbased learning strategy, the traditional wild horse optimization (WHO) approach and an enhanced WHO (EWHO), to enhance the classification performance. The research objectives are to diagnose and classify chest X-ray images into four types: normal lung, COVID-19-infected lung, lung opacity, and viral pneumonia. The proposed opposition-based learning model improves the accuracy, efficiency, and consistency of diagnostic and treatment decisions. The proposed EWHO-configured LSTM approach attains 97.4% accuracy for an efficient classification system for chest X-ray images. The research highlights the importance of privacy-preserving techniques in healthcare databases to protect sensitive patient information from unauthorized access, breaches, and misuse.

IEViT: An enhanced vision transformer architecture for chest X-ray image classification

This study introduces a novel deep learning network, ResCapsNet (Residual Capsule Network), for classifying patients as having COVID-19, tuberculosis (TB), or pneumonia using chest X-ray images. These networks address limitations of existing methods and achieve high accuracy, showcasing its potential as a valuable tool for automated diagnosis. Additionally, a user-friendly interface was developed for easy access by both healthcare professionals and the general public. Users can upload their chest X-rays (or those of a concerned individual) after signing up for the secure website and receive a predicted diagnosis. This deep learning system analyzes and classifies the uploaded chest X-ray images using a hybrid deep learning model, namely, ResCapsNet. The uploaded image undergoes preprocessing (such as resizing, normalization, etc.) to ensure consistency. The preprocessed image is then fed into the hybrid model, which combines improved residual modules for feature extraction and capsule layers for final classification.

The development of automatic chest X-ray (CXR) disease classification algorithms is significant for diagnosing thoracic diseases. Owing to the characteristics of lesions in CXR images, including high similarity in appearance of the disease, varied sizes, and different occurrence locations, most existing convolutional neural network-based methods have insufficient feature extraction for thoracic lesions and struggle to adapt to changes in lesion size and location. To address these issues, this study proposes a high-resolution classification network with dynamic convolution and coordinate attention (HRCC-Net). In the method, this study suggests a parallel multi-resolution network in which a high-resolution branch acquires essential detailed features of the lesion and multi-resolution feature swapping and fusion to obtain multiple receptive fields to extract complicated disease features adequately. Furthermore, this study proposes dynamic convolution to enhance the network's ability to represent multi-scale information to accommodate lesions of diverse scales. In addition, this study introduces a coordinate attention mechanism, which enables automatic focus on pathologically relevant regions and capturing the variations in lesion location. The proposed method is evaluated on ChestX-ray14 and CheXpert datasets. The average AUC (area under ROC curve) values reach 0.845 and 0.913, respectively, indicating this method's advantages compared with the currently available methods. Meanwhile, with its specificity and sensitivity to measure the performance of medical diagnostic systems, the network can improve diagnostic efficiency while reducing the rate of misdiagnosis. The proposed algorithm has great potential for thoracic disease diagnosis and treatment.

Early diagnosis of thorax diseases is very important for saving lives. Recently, deep learning approaches are applied to multilabel classification of chest X-ray images, which yields an emerging field of AI diagnosis, However, so far, there are still some performance problems when using it in hospitals and clinics. This study aims to compare and analyze different ways to balance data for image classification, as well as propose a method of combining different models to improve classification performance. Furthermore, a web diagnostic system introduced the results of this study is implemented. It will contribute to helping doctors to detect chest diseases more accurately, easily, and quickly.

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques

The COVID-19 pandemic, also known as the coronavirus pandemic, is one of a major outbreak spreading across many countries around the world. It impacts severely on the health and life of many people all around the world. Medical imaging is a widely accepted technique for the early detection and diagnosis of disease that includes different techniques such as X-ray, computed tomography (CT) scan etc. For diagnosis COVID-19, chest X-ray is the imaging technique that plays an important role. In the recent years, deep neural networks have been successfully applied in many computer vision tasks including medical imaging. In this paper, we have experimented and evaluated DenseNet model for the classification of COVID-19 chest X-ray images. For that, a publicly available dataset contains 6432 chest X-ray images categorizes into 3 classes were used. Transfer learning and fine-tuning is applied for training the three variant of DenseNet model namely DenseNet121, DenseNet169 and DenseNet201. After evaluating the performance, it has been found that DenseNet201 achieved highest validation accuracy i.e. 0.9367 and lowest validation loss i.e. 0.1653 for classification of COVID-19 in chest X-ray images. © 2021 Karadeniz Technical University. All rights reserved.