Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).
Kobe University Graduate School of Medicine（神户大学医学院）；Kyoto University Graduate School of Medicine（京都大学医学院）
Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. Materials and Methods: From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were …
Cupertino High School（库比蒂诺高中）；Stanford University（斯坦福大学）
Coronavirus disease 2019 (COVID-19) is the highly contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The standard diagnostic testing procedure for COVID-19 is testing a nasopharyngeal swab for SARS-CoV-2 nucleic acid using a real-time polymerase chain reaction (PCR), which can take multiple days to provide a diagnosis. Another widespread form of testing is rapid antigen testing, which has a low sensitivity compared to PCR, but is favored for its quick diagnosis time of usually 15-30 minutes. Patients who test positive for COVID-19 demonstrate diffuse alv…
Northwestern Polytechnical University（西北工业大学）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；The University of Adelaide（阿德莱德大学）；GreyBird Ventures, LLC（GreyBird Ventures有限责任公司）；JF Healthcare Inc.（JF Healthcare公司）
Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention, particularly when other chest imaging modalities are less available. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images. The evolution of viruses and the emergence of novel mutated viruses further result in substantial dataset shift, which greatl…
Over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. To that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease…
Around 450 million people are affected by pneumonia every year which results in 2.5 million deaths. Covid-19 has also affected 181 million people which has lead to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X-ray) and covid-19 (RT-PCR) require the presence of expert radiologists and time, respectively. With the help of Deep Learning models, pneumonia and covid-19 can be detected instantly from Chest X-rays or CT scans. This way, the pro…
University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Bangabandhu Sheikh Mujib Medical University（邦班杜·谢赫·穆吉布医科大学）；North South University（北南大学）；Qatar Armed Forces-Academic Bridge Program（卡塔尔武装部队-学术桥梁计划）；Qatar Foundation（卡塔尔基金会）
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet wer…
The world is still overwhelmed by the spread of the COVID-19 virus. With over 250 Million infected cases as of November 2021 and affecting 219 countries and territories, the world remains in the pandemic period. Detecting COVID-19 using the deep learning method on CT scan images can play a vital role in assisting medical professionals and decision authorities in controlling the spread of the disease and providing essential support for patients. The convolution neural network is widely used in the field of large-scale image recognition. The current method of RT-PCR to diagnose COVID-19 is time-…
School of Computing, University of Utah（犹他大学计算机学院）；NVIDIA Corporation（英伟达公司）；Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center（美国国立卫生研究院临床中心放射科与影像科学部）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；National Institutes of Health（美国国立卫生研究院）
Localization and characterization of diseases like pneumonia are primary steps in a clinical pipeline, facilitating detailed clinical diagnosis and subsequent treatment planning. Additionally, such location annotated datasets can provide a pathway for deep learning models to be used for downstream tasks. However, acquiring quality annotations is expensive on human resources and usually requires domain expertise. On the other hand, medical reports contain a plethora of information both about pneumonia characteristics and its location. In this paper, we propose a novel weakly-supervised attentio…
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；Whiston Hospital（惠斯顿医院）；St Helens and Knowsley Teaching Hospitals NHS Trust（圣海伦斯和诺斯利教学医院国民保健服务信托）；University of Sheffield（谢菲尔德大学）
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing commonly used visual explanation methods are either too noisy or imprecise, wi…
Indian Institute of Technology, Jammu（印度理工学院贾姆姆分校）；University of Pennsylvania（宾夕法尼亚大学）
We propose a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community-Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images. In the first stage, an infection - COVID-19 or CAP, is detected using a pre-trained DenseNet architecture. Then, in the second stage, a fine-grained three-way classification is done using EfficientNet architecture. The proposed COVID+CAP-CNN framework achieved a slice-level classification accuracy of over 94% at identifying COVID-19 and CAP. Further, the proposed framework has the potential t…
With the advancement in AI, deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The propo…
Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications
Amity Institute of Bio Technology, Amity University Kolkata（阿米蒂大学加尔各答分校阿米蒂生物技术学院）；Dept of Computer Science, Heritage Institute of Technology, Kolkata（加尔各答遗产技术学院计算机科学系）；School of Engineering and Technology, Amity University Kolkata（阿米蒂大学加尔各答分校工程与技术学院）
Translating Inference-Time Control to Radiology Vision-Language Models: Activation Steering for Pneumonia Classification on Chest X-rays
Universidade Federal de São Paulo (UNIFESP)（圣保罗联邦大学）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）；Stanford University School of Medicine（斯坦福大学医学院）；DASA；University of Texas Southwestern Medical Center (UTSW)（德克萨斯大学西南医学中心）；Eden
In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only \emph{open-source} deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; ou…
Harrisburg University of Science and Technology（哈里斯堡科技大学）
Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely used by radiologists to detect pneumonia. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. In this work, we propose using transfer learning, which can reduce the neural network…
University of Electronic Science and Technology of China（电子科技大学）；Affiliated Hospital of Chengdu University（成都大学附属医院）
Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia has been greatly improved, but there are still some challenges due to the fuzzy appearance of chest X-rays. In this paper, we propose a deep learning framework named Attention-Based Contrastive Learning for Class-Imbalanced X-Ray Pneumonia Lesion Recognition (denoted as Deep Pneumonia). We adopt self-supervised contrastive learning strategy to pre-train the model without using extra pneumonia data for fully mining the …
Taiwan AI Labs（台湾人工智能实验室）；National Taiwan University Hospital（国立台湾大学医学院附设医院）；Taipei Medical University Hospital（台北医学大学附设医院）；National Health Insurance Administration, Ministry of Health and Welfare（卫生福利部中央健康保险署）；Centers for Disease Control, Ministry of Health and Welfare（卫生福利部疾病管制署）；Taiwan Executive Yuan（台湾行政院）；National Taiwan University College of Medicine（国立台湾大学医学院）
We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specifici…
Elsevier Science Publishers B.V.（爱思唯尔科学出版公司）；University of Winchester（温彻斯特大学）；Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）；University of Leicester（莱斯特大学）
Respiratory diseases kill million of people each year. Diagnosis of these pathologies is a manual, time-consuming process that has inter and intra-observer variability, delaying diagnosis and treatment. The recent COVID-19 pandemic has demonstrated the need of developing systems to automatize the diagnosis of pneumonia, whilst Convolutional Neural Network (CNNs) have proved to be an excellent option for the automatic classification of medical images. However, given the need of providing a confidence classification in this context it is crucial to quantify the reliability of the model's predict…
In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality i…
Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）
The possibility of high-precision and rapid detection of pathologies on chest X-rays makes it possible to detect the development of pneumonia at an early stage and begin immediate treatment. Artificial intelligence can speed up and qualitatively improve the procedure of X-ray analysis and give recommendations to the doctor for additional consideration of suspicious images. The purpose of this study is to determine the best models and implementations of the transfer learning method in the binary classification problem in the presence of a small amount of training data. In this article, various …
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN)（墨西哥国立理工学院计算机研究中心）；Grand Asian University of Sialkot（锡亚尔科特大亚洲大学）；The Islamia University of Bahawalpur（巴哈瓦尔布尔伊斯兰大学）；Zhejiang Normal University（浙江师范大学）；Leads University Lahore（拉合尔利兹大学）
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西达赛奈医学中心生物医学成像研究所）；Cedars-Sinai Medical Center（西达赛奈医学中心）；Semmelweis University（塞梅尔维斯大学）；Azienda Ospedaliero-Universitaria di Parma（帕尔马大学医院）；Monash Health（莫纳什医疗中心）；Centro Cardiologico Monzino IRCCS, University of Milan（米兰大学蒙齐诺心脏中心IRCCS）；Showa University School of Medicine（昭和大学医学院）
Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for rapid quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performe…
Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai Jiao Tong University（上海交通大学）
Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learni…
Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）
The ongoing crisis of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), 4 million people have died due to this disease, whereas there have been more than 180 million confirmed cases of COVID-19. The collapse of the health system in many countries has demonstrated the need of developing tools to automatize the diagnosis of the disease from medical imaging. Previous studies have used deep learning for this purpose. However, the performance of this alternative highly depends on the size of the dataset employed for training th…
Dublin City University（都柏林城市大学）；Trinity College Dublin（都柏林圣三一大学）
The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and clas…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challeng…
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and autom…
University of Saskatchewan（萨斯喀彻温大学）；International Road Dynamics（国际道路动力学公司）；National Institute of Technology, Trichy（特里奇国家理工学院）
One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper,…
University of Leeds（利兹大学）；Leeds Teaching Hospitals NHS Trust（利兹教学医院 NHS 信托基金）
Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifyin…
Zonguldak Bulent Ecevit University（宗古尔达布伦特·埃杰维特大学）
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural netwo…
Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the pneumonia-like effect in the lungs, the examination of chest x-rays can help to diagnose the diseases. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features may not be appropriate to represent chest x-rays as they have a few semantic regions. Though the Bag of Visual Words (BoVW) based features are shown to be more appropriate for x-ray type of images, existing …
The worldwide spread of pneumonia caused by a novel coronavirus poses an unprecedented challenge to the world's medical resources and prevention and control measures. Covid-19 attacks not only the lungs, making it difficult to breathe and life-threatening, but also the heart, kidneys, brain and other vital organs of the body, with possible sequela. At present, the detection of COVID-19 needs to be realized by the reverse transcription-polymerase Chain Reaction (RT-PCR). However, many countries are in the outbreak period of the epidemic, and the medical resources are very limited. They cannot p…
Scuola Normale Superiore（比萨高等师范学校）；National Institute of Nuclear Physics (INFN)（国家核物理研究所）；University of Pavia（帕维亚大学）；University of Pisa（比萨大学）；University of Sassari（萨萨里大学）；University of Florence（佛罗伦萨大学）
The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung…
An Explainable Non-local Network for COVID-19 Diagnosis
Chengdu University of Information Technology（成都信息工程大学）；Southwest Jiaotong University（西南交通大学）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Purdue University（普渡大学）；University at Buffalo, SUNY（纽约州立大学布法罗分校）；West China Hospital, Sichuan University（四川大学华西医院）
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumoni…
Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering (CIISE)（康考迪亚信息系统工程研究所）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；New York University（纽约大学）；University of Montreal（蒙特利尔大学）；Iran university of medical science（伊朗医科大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心研究所）
The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive …
