scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals A Transfer Learning Method for Pneumonia Classification and Visualization

2020 ◽  
Vol 10 (8) ◽  
pp. 2908 ◽  
Author(s):  
Juan Luján-García ◽  
Cornelio Yáñez-Márquez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto
Keyword(s):  
Transfer Learning ◽  
Causes Of Death ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray ◽  
Aided Diagnosis ◽  
Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

scholarly journals NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 775
Author(s):  
Juan Eduardo Luján-García ◽  
Yenny Villuendas-Rey ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
X Rays ◽  
Huge Amount ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Radiological Studies

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

scholarly journals COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ali Farki ◽  
Zahra Salekshahrezaee ◽  
Arash Mohammadi Tofigh ◽  
Reza Ghanavati ◽  
Behdad Arandian ◽  
...  
Keyword(s):  
State Of The Art ◽  
Optimal Method ◽  
X Ray ◽  
Suggested Technique ◽  
Improve Accuracy ◽  
Sensitivity Rate ◽  
Chest X Ray ◽  
Therapeutic Measures ◽  
Simulation Results ◽  
Day By Day

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others.

Chest X-Ray Image-Based Testing Using Machine Learning Techniques

Covid-19 ◽  
2021 ◽  
pp. 241-278
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Alaknanda Ashok ◽  
Amit Dumka ◽  
Anuj Bhardwaj
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax

2020 ◽  
pp. 084653712090885
Author(s):  
Fatemeh Homayounieh ◽  
Subba R. Digumarthy ◽  
Jennifer A. Febbo ◽  
Sherief Garrana ◽  
Chayanin Nitiwarangkul ◽  
...  
Keyword(s):  
False Negative ◽  
Tension Pneumothorax ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Chest Radiographs ◽  
Detection Rates ◽  
X Ray ◽  
Institutional Review ◽  
Type Area ◽  
Chest X Ray

Purpose: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR). Method and Materials: Our retrospective institutional review board–approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection. Results: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs ( P < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99). Conclusion: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax. Clinical Relevance/Application: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.

scholarly journals COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5683 ◽  
Author(s):  
Lourdes Duran-Lopez ◽  
Juan Pedro Dominguez-Morales ◽  
Jesús Corral-Jaime ◽  
Saturnino Vicente-Diaz ◽  
Alejandro Linares-Barranco
Keyword(s):  
Deep Learning ◽  
Imaging Techniques ◽  
Learning System ◽  
Screening Process ◽  
X Ray ◽  
Average Accuracy ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Control ◽  
Aided Diagnosis

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

scholarly journals COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

2020 ◽  
Vol 2 (4) ◽  
pp. 490-504
Author(s):  
Md Manjurul Ahsan ◽  
Kishor Datta Gupta ◽  
Mohammad Maminur Islam ◽  
Sajib Sen ◽  
Md. Lutfar Rahman ◽  
...  
Keyword(s):  
Ct Scan ◽  
Initial Screening ◽  
X Ray ◽  
Learning Techniques ◽  
Alternative Approach ◽  
Number Of Patients ◽  
The Usa ◽  
Chest X Ray ◽  
Interpretable Model ◽  
Potential Opportunity

The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach. Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests simultaneously to detect patients with COVID-19 symptoms. In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest X-ray images. Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used. Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.

scholarly journals A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1146 ◽  
Author(s):  
Ahmed T. Sahlol ◽  
Mohamed Abd Elaziz ◽  
Amani Tariq Jamal ◽  
Robertas Damaševičius ◽  
Osama Farouk Hassan
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Computational Time ◽  
X Ray ◽  
Artificial Ecosystem ◽  
Benchmark Datasets ◽  
Chest X Ray ◽  
Medical Image Recognition ◽  
Novel Method ◽  
Tb Diagnostics

Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

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