scholarly journals Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning

2020 ◽  
Author(s):  
Md. Milon Islam ◽  
Md. Zabirul Islam ◽  
Amanullah Asraf ◽  
Weiping Ding
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Medical Images ◽  
Imaging Techniques ◽  
Good Alternative ◽  
Computational Time ◽  
X Rays ◽  
Pcr Method ◽  
Polymerase Chain ◽  
Complex Features

The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy and computational time in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

scholarly journals Prediction of Locations in Medical Images Using Orthogonal Neural Networks

2021 ◽  
Author(s):  
Jong Soo Kim ◽  
Yongil Cho ◽  
Tae Ho Lim
Keyword(s):  
Neural Network ◽  
Diagnostic Performance ◽  
Medical Images ◽  
Selection Criterion ◽  
Activation Function ◽  
X Rays ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Image Localization

Abstract An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved; the ONN outperformed the CNN. The diagnostic performance of the ONN with a sigmoid activation function for all the nodes obviously outperformed the ONN with the rectified linear unit (RELU) activation function for all the nodes other than the output nodes. In addition, by applying ONN and CNN to predict the location of the glottis in laryngeal images, we achieved accurate and adjacent prediction rates of 70.5% and 20.5%, respectively, with the ONN. The prediction accuracy of the ONN was compared favorably with that of the CNN. Compared to a CNN, an ONN required only approximately 10% of the computations using a CNN trained on images with an input resolution of 256 × 256 pixels. A fully-connected small artificial neural network (ANN), selected by comparing the test results of several dozens of small ANN models, achieved the best location prediction performance on medical images. This study demonstrated that an ONN can be used as a quick selection criterion to compare ANN models for image localization since an ONN performed well compared decently with the selected ANN model.

scholarly journals RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning Research

2021 ◽  
Author(s):  
Yang Yang ◽  
Xueyan Mei ◽  
Philip Robson ◽  
Brett Marinelli ◽  
Mingqian Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Images ◽  
Bone Age ◽  
Natural World ◽  
X Rays ◽  
Starting Point ◽  
Size Diversity

Abstract Most current medical imaging Artificial Intelligence (AI) relies upon transfer learning using convolutional neural networks (CNNs) created using ImageNet, a large database of natural world images, including cats, dogs, and vehicles. Size, diversity, and similarity of the source data determine the success of the transfer learning on the target data. ImageNet is large and diverse, but there is a significant dissimilarity between its natural world images and medical images, leading Cheplygina to pose the question, “Why do we still use images of cats to help Artificial Intelligence interpret CAT scans?”. We present an equally large and diversified database, RadImageNet, consisting of 5 million annotated medical images consisting of CT, MRI, and ultrasound of musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, and pulmonary pathologies over 450,000 patients. The database is unprecedented in scale and breadth in the medical imaging field, constituting a more appropriate basis for medical imaging transfer learning applications. We found that RadImageNet transfer learning outperformed ImageNet in multiple independent applications, including improvements for bone age prediction from hand and wrist x-rays by 1.75 months (p<0.0001), pneumonia detection in ICU chest x-rays by 0.85% (p<0.0001), ACL tear detection on MRI by 10.72% (p<0.0001), SARS-CoV-2 detection on chest CT by 0.25% (p<0.0001) and hemorrhage detection on head CT by 0.13% (p<0.0001). The results indicate that our pre-trained models that are open-sourced on public domains will be a better starting point for transfer learning in radiologic imaging AI applications, including applications involving medical imaging modalities or anatomies not included in the RadImageNet database.

scholarly journals Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks

2021 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Buyut Khoirul Umri ◽  
Ema Utami ◽  
Mei P Kurniawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Literature Review ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Image Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19

scholarly journals Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

2020 ◽  
Vol 10 (9) ◽  
pp. 3233 ◽  
Author(s):  
Tawsifur Rahman ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Khandaker R. Islam ◽  
Khandaker F. Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Viral Pneumonia ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
The Right

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 at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in 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 were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

scholarly journals LungAI: A Deep Learning Convolutional Neural Network for Automated Detection of COVID-19 from Posteroanterior Chest X-Rays

2020 ◽  
Author(s):  
Aryan Gulati
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Antiviral Treatment ◽  
Automated Detection ◽  
Nasopharyngeal Swab ◽  
X Rays ◽  
Detection Model ◽  
Mode Of Detection ◽  
Polymerase Chain

AbstractCOVID-19 is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). As of December 2020, more than 72 million cases have been reported worldwide. The standard method of diagnosis is by Real-Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) from a Nasopharyngeal Swab. Currently, there is no vaccine or specific antiviral treatment for COVID-19. Due to rate of spreading of the disease manual detection among people is becoming more difficult because of a clear lack of testing capability. Thus there was need of a quick and reliable yet non-labour intensive detection technique. Considering that the virus predominantly appears in the form of a lung based abnormality I made use of Chest X-Rays as our primary mode of detection. For this detection system we made use of Posteroanterior (PA) Chest X-rays of people infected with Bacterial Pneumonia (2780 Images), Viral Pneumonia (1493 Images), Covid-19 (729 Images) as well as those of perfectly Healthy Individuals (1583 Images) procured from various Publicly Available Datasets and Radiological Societies. LungAI is a novel Convolutional Neural Network based on a Hybrid of the DarkNet and AlexNet architecture. The network was trained on 80% of the dataset with 20% kept for validation. The proposed Coronavirus Detection Model performed exceedingly well with an accuracy of 99.16%, along with a Sensitivity value of 99.31% and Specificity value of 99.14%. Thus LungAI has the potential to prove useful in managing the current Pandemic Situation by providing a reliable and fast alternative to Coronavirus Detection given strong results.

scholarly journals Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

2021 ◽  
Author(s):  
Japman Singh Monga ◽  
Yuvraj Singh Champawat ◽  
Seema Kharb
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
X Rays ◽  
Performance Parameters ◽  
Chain Reaction ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

scholarly journals A 3D-2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Douglas Omwenga Nyabuga ◽  
Jinling Song ◽  
Guohua Liu ◽  
Michael Adjeisah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Spatial Data ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Experimental Result ◽  
Computational Time ◽  
Hyperspectral Image Classification

As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.

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