Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Medical Image Diagnosis Using Deep Learning Classifiers for COVID-19

COVID-19 and other diseases must be precisely and swiftly classified to minimize disease spread and avoid overburdening the healthcare system. The main purpose of this study is to develop deep-learning classifiers for normal, viral pneumonia, and COVID-19 disorders using CXR pictures. Deep learning image classification algorithms are used to recognize and categorise image data to detect the presence of illnesses. The raw image must be pre-processed since deep neural networks perform the most important aspect of medical image identification, which includes translating the raw image into an intelligible format. The dataset includes three classifications, including normal and viral pneumonia and COVID-19. To aid in quick diagnosis and the proposed models leverage the performance validation of several models, which are summarised in the form of a recall, Fl-score, precision, accuracy, and AUC, to distinguish COVID-19 from other types of pneumonia. When all the deep learning classifiers and performance parameters were analyzed, the ResNetl0lV2 achieved the highest accuracy of COVID-19 classifications is 97.S2%, ResNetl0lV2 had the greatest accuracy of the normal categorization is 92.04% and the Densenet201 had the greatest accuracy of the pneumonia classification is 99.92%. The suggested deep learning system is an excellent choice for clinical use to aid in the COVID-19, normal, and pneumonia processes for diagnosing infections using CXR scans. Furthermore, the suggested approaches provided a realistic technique to implement in real-world practice, assisting medical professionals in diagnosing illnesses from CXR images. © 2023 IEEE.

Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system. © 2022 IEEE.

Improving Robustness of Medical Image Diagnosis System by Using Multi-loss Hybrid Adversarial Function with Heuristic Projection

Medical image diagnosis system by using deep neural networks (DNN) can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening. However, DNN based medical image diagnosis is known to be influenced by the adversarial perturbations. In order to improve the robustness of medical image diagnosis system, this paper proposes an adversarial attack training method by using multi-loss hybrid adversarial function with heuristic projection. Firstly, the effective adversarial attacks which contain the noise style that can puzzle the network are created with a multi-loss hybrid adversarial function (MLAdv). Then, instead of adding these adversarial attacks to the training data directly, we consider the similarity between the original samples and adversarial attacks by using an adjacent loss during the training process, which can improve the robustness and the generalization of the network for unanticipated noise perturbations. Experiments are finished on COVID-19 dataset. The average attack success rate of this method for three DNN based medical image diagnosis systems is 63.9%, indicating that the created adversarial attack has strong attack transferability and can puzzle the network effectively. In addition, with the adversarial attack training, the augmented networks by using adversarial attacks can improve the diagnosis accuracy by 4.75%. Therefore, the augmented network based on MLAdv adversarial attacks can improve the robustness of medical image diagnosis system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Effect of Data Augmentation and Depth of networks on CNN-based Breast X-ray Image Diagnosis of Pneumonia

COVID-19 has become a worldwide disease that draws researchersҠattention, and people have tried deep learning based methods to boost the diagnosis. It is crucial to design an algorithm for automatic Pneumonia Diagnosis. In this paper, we trained and applied a CNN-based model that is competent to not only diagnose COVID-19 based on breast x-ray images but also do classification on 4 classes, namely normal, COVID-19, viral pneumonia, and bacterial pneumonia. Further, we dive into the effect of data augmentation and depth of networks, and weӶe gained surprising results. © 2022 SPIE.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

COVID-19 Image Diagnosis on CT Images Using Deep Learning

A dangerous and contagious respiratory disease, COVID-19, is currently spreading quickly around the world. One of the important approaches to slow down the spread is to detect those who have COVID-19 and ask them to self-quarantine. The hospitals use CT scans to diagnose COVID-19, and Artificial Intelligence could diagnose as well. Our study collects around 300 images including both those belong to COVID-19 patients and normal people from Kaggle. We run both CNN and FFNN models on the data and record the accuracy together with the F1 score of each model. It turns out that the accuracy for CNN is 100%, while for FFNN is 96.88%. CNN has a lower testing loss, and it takes less time to train. The result has shown that these models could accurately predict the correct one, but there are some drawbacks. The 100% accuracy might indicate overfitting. Things that the study could improve upon include collecting more data and adding new classifications. © 2021 IEEE.

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans.

In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

COVID-19 Diagnosis Using Chest X-ray Images via Classification and Object Detection

We diagnose the symptoms and the localization of the affected area in COVID-19 cases/patients using chest x-ray images provided by the Kaggle competition. By training and predicting symptoms and the localization of the affected area using the YOLOv5 object detection algorithm, we obtained a low accuracy of approximately 20%. However, we improved the accuracy to approximately 80% by using the image classification model Keras / EfficientNetB7, in addition to YOLOv5. Although it is difficult to detect visually ambiguous objects such as pneumonia, we believe that we can improve the accuracy by training/predicting symptoms using the image classification model and the localization of the affected area using the object detection algorithm. © 2021 ACM.

CNN-based diagnosis of COVID-19 Pneumonia: A comparative study on different image preprocessing methods

CT image diagnosis of COVID-19, an infectious disease that causes respiratory problems, proved efficient with CNN-based methods. The accuracy of these machine learning methods relies on the quality and dispersion of the training set, which has often been ensured by utilizing the preprocessing strategies. However, few studies investigated the impact of different preprocessing methods on accuracy rates in diagnosing COVID-19. As a result, a comparative study on different image preprocessing methods was done in this work. Two popular preprocessing methods contrast limited adaptive histogram equalization (CLAHE) and Discrete Cosine Transform (DCT), which were processed and compared in a CNN-based diagnosis framework. With a mixed and open-source dataset, the experimental results showed that DCT based preprocessing method had a higher accuracy on the test set, which was 92.71%. © 2021 IEEE.

Study of VGG-19 Depth in Transfer Learning for COVID-19 X-Ray Image Classification

Modern-era largely depends on Deep Learning (DL) in a lot of applications. Medical Images Diagnosis is one of the important fields nowadays because it is related to human life. But this DL requires large datasets as well as powerful computing resources. At the beginning of 2020, the world faced a new pandemic called COVID-19. Since it is new, shortage of reliable datasets of a running pandemic is a common phenomenon. One of the best solutions to mitigate this shortage is taking advantage of Deep Transfer Learning (DTL). DTL would be useful because it learns from one task and could work on another task with a smaller amount of dataset. This paper aims to examine the application of the transferred VGG-19 to solve the problem of COVID-19 detection from a chest x-ray. Different scenarios of the VGG-19 have been examined, including shallow model, medium model, and deep model. The main advantages of this work are two folds: COVID-19 patient can be detected with a small number of data sets, and the complexity of VGG-19 can be reduced by reducing the number of layers, which consequently reduces the training time. To assess the performance of these architectures, 2159 chest x-ray images were employed. Reported results indicated that the best recognition rate was achieved from a shallow model with 95% accuracy while the medium model and deep model obtained 94% and 75%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Effective approach for implementing COVID-19 CT Lung Segmentation using Low-Cost System on Chip

Computer vision techniques always had played a salient role in numerous medical fields, especially in image diagnosis. Amidst a global pandemic situation, one of the archetypal methods assisting healthcare professionals in diagnosing various types of lung cancers, heart diseases, and COVID-19 infection is the Computed Tomography (CT) medical imaging technique. Segmentation of Lung and Infection with high accuracy in COVID-19 CT scans can play a vital role in the prognosis and diagnosis of a mass population of infected patients. Most of the existing works are predominately based on large private data sets that are practically impossible to obtain during a pandemic situation. Moreover, it is difficult to compare the segmentation methods as the data set are obtained in various geographical areas and developed and implemented in different environments. To help the current global pandemic situation, we are proposing a highly data-efficient method that gets trained on 20 expert annotated COVID-19 cases. To increase the efficiency rate further, the proposed model has been implemented on NVIDIA-Jetson Nano (System-on-Chip) to completely exploit the GPU performance for a medical application machine learning module. To compare the results, we tested the performance with conventional U-Net architecture and calculated the performance metrics. The proposed state-of-art method proves better than the conventional architecture delivering a Dice Similarity Coefficient of 99%. © 2021 IEEE.

Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays.

Deep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.•This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance.•The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19.•The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices.

A preliminary study on establishment of AI-assisted remote imaging diagnosis system for major infectious diseases / 中华医学科研管理杂志

Objective@#In response to the outbreak of severe infectious diseases such as new coronavirus pneumonia, an artificial intelligence based image diagnosis system is established to improve the efficiency of disease diagnosis, reduce the burden of front-line doctors, and improve the medical resource allocation.@*Methods@#Using the deep convolution neural network and regional prevention and control auxiliary information system, the image data and other information of the confirmed patients were analyzed and processed comprehensively.@*Results@#A set of AI based medical image auxiliary data processing system is proposed. Combined with multimodal medical data collaborative diagnosis, it can effectively and accurately segment the diseased areas in patients' lung CT images, and generate standardized reports. Relying on the multi-center collaborative diagnosis and treatment platform, the system can introduce multi-expert remote consultation mechanism to improve the diagnosis quality of severe patients.@*Conclusions@#By segmenting pathological regions, generating standardized reports and introducing multicenter mechanism, the system can help to optimize the medical resources allocation and improve the utilization of these resources.