scholarly journals Deteksi COVID-19 Berdasarkan Hasil Rontgen Dada (Chest Xray) Menggunakan Python

2021 ◽  
Vol 1 (1) ◽  
pp. 58-67
Author(s):  
Pebri Andhi Herry Pratama ◽  
Rony Teguh ◽  
Abertun Sagit Sahay ◽  
Valencia Wilentine
Keyword(s):  
Confusion Matrix ◽  
X Rays ◽  
Chest Computed Tomography ◽  
X Ray ◽  
Data Prediction ◽  
Testing Stage ◽  
Architectural Model ◽  
Chest X Ray ◽  
Lung Infections

Chest X-ray have an important function in the three areas of healthcare, namely diagnosis, treatment and re-examination. Studies from China suggest Chest Radiographs (X-Ray) and Chest Computed Comography (CT) scans can help diagnose COVID-19. Therefore, chest x-rays (x-rays) and chest computed tomography (CT) are appropriate methods for lung infections due to COVID-19. Based on this, the authors tried to make a model for the classification of digital images of Chest X-Ray results with the labels of Normal, Pneumonia, Tuberculosis (TBC), and COVID-19. Through the resulting model, the best model to use will be compared. The method used to create this model is through training and testing the dataset using the Convolutional Neural Network (CNN) architectural model, namely VGG19, ResNet50, and InceptionV3. The number of images used is 1,000 Chest X-Ray images. The dataset is divided into training and validation datasets in several ratios of 20% : 80%, 50%: 50%, and 80%: 20%. While testing uses 10% from train datset chest x-ray images as a confusion matrix dataset and 4 chest x-ray images as a prediction dataset. From the results of the research that has been done, the best model is VGG19 at 41 of 61 epoch and a ratio of 20% : 80%. Where the VGG19 model produces 94.44% for accuracy and 0,1084 loss value for training. Whereas at the testing stage with a configuration matrix, 95% accuracy value was obtained. Then for testing the new data prediction produces the best accuracy with 98.97% accuracy for the Normal label, 99.16% for COVID-19, 99.56% for Pneumonia, and 99.79% for Tuberculosis (TBC).

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy ◽  
Wei Pang
Keyword(s):  
Disease Progression ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Activation Mapping ◽  
Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

scholarly journals Digital Image Segmentation Resulting from X-Rays of Covid Patients using K-Means and Extraction Features Method

2021 ◽  
Vol 3 (1) ◽  
pp. 25-28
Author(s):  
Dhian Satria Yudha Kartika ◽  
Anita Wulansari ◽  
Hendra Maulana ◽  
Eristya Maya Safitri ◽  
Faisal Muttaqin
Keyword(s):  
Digital Image ◽  
Confusion Matrix ◽  
Health Sector ◽  
X Rays ◽  
Social Psychological ◽  
Uniform Size ◽  
X Ray ◽  
X Ray Imaging ◽  
Standard Quality ◽  
Chest X Ray

The COVID-19 pandemic has significant impact on people's lives such as economic, social, psychological and health conditions. The health sector, which is spearheading the handling of the outbreak, has conducted a lot of research and trials related to COVID-19. Coughing is a common symptoms among humans affected by COVID-19 in earlier stage. The first step when a patient shows symptoms of COVID-19 was to conduct a chest x-ray imaging. The chest x-rayss can be used as a digital image dataset for analysing  the spread of the virus that enters the lungs or respiratory tract. In this study, 864 x-rays  were used as datasets. The images were still raw, taken directly from Covid-19 patients, so there were still a lot of noise. The process to remove unnecessary images would be carried out in the pre-processing stage. The images used as datasets were not mixed with the background which can reduce the value at the next stage. All datasets were made to have a uniform size and pixels to obtain a standard quality and size in order to support the next stage, namely segmentation. The segmentation stage of the x-ray datasets of Covid-19 patients was carried out using the k-means method and feature extraction. The Confusion Matrix method used as testing process. The accuracy value was 78.5%. The results of this testing process were 78.5% of precision value, 78% of recall and  79% for f-measure

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy
Keyword(s):  
Disease Progression ◽  
Input Image ◽  
Mapping Technique ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Chest X Ray ◽  
Activation Mapping

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

scholarly journals COVID-19 detection in Chest X-ray Images using Deep Learning

2020 ◽  
Author(s):  
Ebru Erdem ◽  
Tolga Aydın
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
Empirical Evaluation ◽  
Specific Information ◽  
The Novel ◽  
X Ray ◽  
Chest X Ray ◽  
Lung Infections ◽  
Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All the analyses were performed on a publicly available CXR dataset. This study offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of a disease. Empirical evaluation demonstrates that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.

scholarly journals Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

2021 ◽  
Vol 11 (22) ◽  
pp. 10528
Author(s):  
Khin Yadanar Win ◽  
Noppadol Maneerat ◽  
Syna Sreng ◽  
Kazuhiko Hamamoto
Keyword(s):  
Deep Learning ◽  
Area Under Curve ◽  
Ensemble Classifier ◽  
Saliency Map ◽  
X Rays ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Chest X Ray

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

scholarly journals Chest X-ray classification using Deep learning for automated COVID-19 screening

2020 ◽  
Author(s):  
Ankita Shelke ◽  
Madhura Inamdar ◽  
Vruddhi Shah ◽  
Amanshu Tiwari ◽  
Aafiya Hussain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
X Rays ◽  
X Ray ◽  
The People ◽  
History Of ◽  
Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

scholarly journals COVID-19 detection in Chest X-ray Images using Deep Learning

2020 ◽  
Author(s):  
Ebru Erdem ◽  
Tolga Aydın
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
Specific Information ◽  
The Novel ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Lung Infections ◽  
Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

scholarly journals COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Biology ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1174
Author(s):  
Shamima Akter ◽  
F. M. Javed Mehedi Shamrat ◽  
Sovon Chakraborty ◽  
Asif Karim ◽  
Sami Azam
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Statistical Significance ◽  
Confusion Matrix ◽  
Original Data ◽  
Rank Test ◽  
X Rays ◽  
X Ray ◽  
Professional Exposure ◽  
Chest X Ray

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

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