can pneumonia look like cancer on x ray

The results of the current study only show that our CADx system could achieve high accuracy in the public datasets. He, K., Zhang, X., Ren, S. & Sun, J. Many people have lung nodules. B. Radiology https://doi.org/10.1148/radiol.2020201160 (2019). Splitting of training/validation/test sets was used when building and evaluating the CADx system. Furthermore, the proposed ensemble model is domain-independent and thus can be applied to a large variety of computer vision tasks. In such cases, doctors use air bronchogram signs to detect pneumonia. A wrongful pneumonia diagnosis could prove fatal. WHO | Novel CoronavirusChina. The results for each fold and the average and standard deviation values over the five folds are tabulated in Table 4 for the Kermany dataset [4] and in Table 5 for the RSNA challenge dataset [33]. The proposed ensemble framework predicted Normal (correct classification) with a confidence rate of 68.1 (b) Case-2: GoogLeNet predicted Normal with a confidence score of 98.6%, ResNet-18 predicted Pneumonia with a confidence score of 58.3%, and DenseNet-121 predicted Pneumonia with a confidence score of 69.3%. For image normalization, CXR images were divided by 255, and pixel values of them ranged from 0 to 1. Most lung nodules are not a sign of lung cancer and dont require treatment. The ensemble combination achieved an accuracy rate of 98.81%. Different regions of the X-rays are the focus of the different models that capture complementary information. [19] and Stephen et al. Cohen, J. P., Morrison, P. & Dao, L. COVID-19 image data collection (2020). Ensemble learning [31, 32] allows the decisions generated by multiple CNN models to be fused, thus effectively incorporating in the ensemble model the salient features of all its base models, capturing complementary information from the different classifiers, and allowing a more robust decision. The aforementioned terms can then be defined as follows. The parameters of mixup was set to 0.113. Early detection of pneumonia is crucial for determining the appropriate treatment of the disease and preventing it from threatening the patients life. They implemented five traditional classifiers: multi-layer perceptron (MLP), random forest, sequential minimal optimization (SMO), classification via regression, and logistic regression. They achieved an 83.61% AUC score on their dataset. Recent advances in machine learning, particularly deep learning with convolutional neural network (CNN), have shown promising performance of CADx in classifying disease patterns on medical images, such as CXR and chest CT8,9,10,11. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India, 3 The proposed model was evaluated on two publicly available chest X-ray datasets, the Kermany dataset [. Contact your healthcare provider if you have lung nodules and start to experience: Most people find out they have a lung nodule after getting an imaging test in preparation for a procedure or another purpose. Fig 14(a) shows a case where a sample belonging to class Normal was misclassified as Pneumonia; the corresponding GradCAM analysis images are shown in parts (c), (d), and (e). In addition, sensitivity of COVID-19 pneumonia was also calculated using the VGG16-based model. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C1A01011131). Data augmentation, however, provides only a limited amount of new information from which the CNNs can learn and thus may not significantly boost their performance. The weight (w(i)) assigned to each classifier is then computed using the hyperbolic tangent function, as shown in Eq 1. If the growth presses against the airway, you may cough, wheeze or struggle to catch your breath. & Others, Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study. Chest. It is necessary to investigate the usefulness of our CADx system using clinical data. Compared with chest CT, the sensitivity of CXR is generally low for pulmonary diseases. Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach, Yue H., Yu Q., Liu C., Huang Y., Jiang Z., Shao C., et al. The red arrows in (b) indicate white infiltrates, a distinguishing feature of pneumonia. [13] applied purely transfer learning approaches in which different CNN models pre-trained on ImageNet [7] data are used for pneumonia classification. 2017-January 22612269 (Institute of Electrical and Electronics Engineers Inc., 2017). The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. The input image size of VGG16 was changed to 220220 pixels. Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Radiology 251, 175184 (2009). Learn. Other types of pre-trained models were compared with the VGG16-based model. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. A chest X-ray is most commonly used to detect abnormalities in the lungs, but can also detect abnormalities in the heart, aorta, and the bones of the thoracic area. Irregular edges are more suspicious. Fig 1 shows an example shows an example of a pneumonic and a healthy lung X-ray. continuous . 2. Fig 12 shows the results of the GradCAM analysis of a pneumonic and a healthy lung X-ray, where all three models were used to form the ensemble. To search for optimal hyperparameters of the VGG16-based model and combination of data augmentation methods, random search was performed19. Case-1: (a) shows an image originally belonging to class Normal but misclassified as Pneumonia by the framework. Kundu R., Basak H., Singh P., Ahmadian A., Ferrara M. & Sarkar R. Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans. Rarely, pulmonary nodules are a sign of lung cancer. To compare with the VGG16-based model, the following four pre-trained models were used for the transfer learning: Resnet-5020, MobileNet21, DenseNet-12122, and EfficientNet23. Janizek J., Erion G., DeGrave A. Early diagnosis of pneumonia is crucial to cure the disease completely. Possible Causes If your radiologist reports that you have a shadow on your lung, your healthcare provider will begin to explore the possible causes based on your symptoms and other health issues. Meraj T., Hassan A., Zahoor S., Rauf H., Lali M., Ali L., et al. In fact, in the transfusion era, pneumonia is the leading cause of death among patients with . Activation functions of the first and last fully-connected layer were rectified linear unit and softmax, respectively. & Lee S. An adversarial approach for the robust classification of pneumonia from chest radiographs. In this case, overfitting may have occurred in external validation. The outbreak of the novel coronavirus disease (COVID-19) started in Wuhan, Hubei province, China at the end of 20191, and COVID-19 spread across the world in 2020. Among the several types of pre-trained models, VGG16 was the most accurate for the 3-category classification. (2017). [14] used the DenseNet-121 CNN model for pneumonia classification but achieved only a 76.8% f1-score for classification. In addition to conventional data augmentation method (such as flipping, shifting, rotating, and etc. They talked me into going to the ER. In contrast to machine learning algorithms, for which handcrafted features need to be extracted and selected for classification or segmentation [27, 28], deep learning-based methods perform end-to-end classification [29, 30], where the relevant and informative features are automatically extracted from the input data and classified. In the meantime, to ensure continued support, we are displaying the site without styles (b) RSNA challenge dataset [33]. Respiratory illnesses and infections can cause nodules to form in the lungs. Pulse oximetry measures how much oxygen is in your blood. Usually follow up x-rays or ct after acute pneumonia clears can be helpful. Before They applied these features in a large number of regression and classification models, such as decision trees, support vector machines, and logistic regression, and compared the results of the models. Kuo et al. By submitting a comment you agree to abide by our Terms and Community Guidelines. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology https://doi.org/10.1148/radiol.2020200823 (2020). & Rodrigues J. Identifying pneumonia in chest X-rays: A deep learning approach. Appointments 216.444.6503. Nodules that stay the same size during a two-year surveillance period are not likely to be cancer. Open in a separate window . The first dataset, the Kermany dataset [4], consists of 5856 chest X-ray images from a large population of both adults and children, unevenly distributed among the classes Pneumonia and Normal. The second dataset was provided by the RSNA [33] and was posed as a Kaggle challenge for pneumonia detection. IEEE Trans. The findings are often a surprise. Very deep convolutional networks for large-scale image recognition. The proposed ensemble framework correctly predicted the sample to be Normal with a confidence score of 66.3%. CXR chest X-ray imaging, COVID-19 novel coronavirus disease. Although the combination of conventional method, mixup, and RICAP was also evaluated, the combination of three methods was not as good as that of proposed method based on the results of random search. To provide you with the most . The same base learners were used in all the ensembles: GoogLeNet, ResNet-18, and DenseNet-121. Case-2: (b) shows an image of class Pneumonia predicted to belong to the Normal class by the framework. Zhang J., Xie Y., Pang G., Liao Z., Verjans J., Li W., et al. The proposed method included the transfer learning, in which CNN models pre-trained on a large dataset is used for the improvement of accuracy and robustness9,12. Just as in people, chest x-rays in dogs are safe and painless and use a small amount of radiation for the purpose of taking a picture of the dog's chest. Federal government websites often end in .gov or .mil. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. Furthermore, to establish the superiority of the proposed ensemble scheme over traditional popular ensemble techniques, the results are compiled in Table 9. These residuals or skip connections perform identity mapping, which neither adds parameters nor increases the computational complexity. Densely connected convolutional networks. Therefore, this study only included adult CXR images of the healthy and non-COVID-19 pneumonia from the RSNA dataset. As previously mentioned, pneumonia affects a large number of individuals, especially children, mostly in developing and underdeveloped countries characterized by risk factors such as overcrowding, poor hygienic conditions, and malnutrition, coupled with the unavailability of appropriate medical facilities. After the convolution layers of VGG16, the global averaging pooling layer, fully-connected layer, and dropout layer were added to VGG16. The inception block is shown in Fig 3(b). However, ensemble models have not been used for classification tasks in the pneumonia detection problem to the best of our knowledge, and, for the first time in this domain, we adopted ensemble learning in this study for the classification of lung X-rays into Pneumonia and Normal classes. Google Scholar. A novel Covid-19 and pneumonia classification method based on F-transform. About 95% of lung nodules are benign. The purpose of this study was to develop CADx system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy using CXR images and CNN. To solve the data scarcity problem in biomedical image classification tasks, transfer learning, wherein knowledge gained from a large dataset is used to fine-tune the model on a current small dataset, is currently a frequently used approach. Wang X., Peng Y., Lu L., Lu Z., Bagheri M. & Summers R. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. 113 (2017). There is a problem with information submitted for this request. Tan, M. & Le, Q. V. EfficientNet: rethinking model scaling for convolutional neural networks. Takahashi, R., Matsubara, T. & Uehara, K. Data augmentation using random image cropping and patching for deep CNNs. Chest X-ray to look for the location and extent of inflammation in your lungs. In the majority voting-based ensemble, the class that obtained the maximum votes from the base learners is predicted as the class of the sample. Zhang, H., Cisse, M., Dauphin, Y. N. & Lopez-Paz, D. Mixup: beyond empirical risk minimization. The layers of VGG16 were sorted in the order of image processing, and all trainable parameters of the 1st10th layers in VGG16 was frozen for transfer learning. To define these evaluation metrics, first, we define the terms True Positive, False Positive, True Negative, and False Negative.. However, bacteria and viruses cause the majority of pneumonia infections. Furthermore, the concatenation of the feature maps from the previous layers with the current layer enhances the feature representation. (c) Fold-3. Dalhoumi S., Dray G., Montmain J., Derosire, G. & Perrey S. An adaptive accuracy-weighted ensemble for inter-subjects classification in brain-computer interfacing. If your doctor suspects you may have COPD, they will likely order a few. production of a lot of mucus or sputum. Our results show that diagnostic accuracy of the 3-category classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy was more than 80% in the proposed method. CNNs are preferred for image data classification because they automatically extract translationally invariant features through the convolution of the input image and filters. Most benign lung nodules dont need treatment. To assure robustness of the models, the 3-category accuracy was calculated 5 times by changing random seed, training the models, and evaluating the test set. Enlarged heart. Over time, a granuloma can calcify or harden in the lung, causing a noncancerous lung nodule. On X-rays or scans, these growths may look like a shadow or spot on the lung. Article Biol. 36th Int. Your healthcare provider may refer to the growth as a spot on the lung, coin lesion or shadow. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Evidently, the different models focused on different regions of the lung X-rays, indicating that the base learners capture complementary information. [22] proposed a confidence-aware module for anomaly detection in lung X-ray images, posing the detection task as a one-class problem (determining only the anomalies). The ensemble learning model helps incorporate the discriminative information of all its constituent models, and thus, its predictions are superior to those of any of its constituent base learners. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. (d) Fold-4. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Deep residual learning for image recognition. & Balas V. AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays. COVID-19 can be detected with the use of real-time polymerase chain reaction (RT-PCR) test of SARS-CoV-2. Helpful clues include your medical history, family history, lab tests, and factors like smoking or exposure to occupational toxins. However, for a particular problem, a certain base classifier may be able to capture information better than others. Physicians use this X-ray image to diagnose or monitor treatment for conditions of pneumonia. A comparative evaluation was conducted to demonstrate the superiority of the proposed method over other models and frequently used ensemble techniques published in the literature. This led to the success of the ensemble approach. Pulmonary lymphoma / lung cancer Pulmonary embolism Tuberculosis (TB) Many of these conditions will grow rapidly worse without prompt and adequate treatment. [16] segmented the lung regions from chest X-ray images and extracted eight statistical characteristics from these regions, which they used to classify them. Deep residual learning for image recognition. This indicates the robustness of the ensemble framework performance. On X-rays or scans, these growths may look like a shadow or spot on the lung. For the healthy lung case shown in Fig 12(d)12(f), the confidence scores are GoogLeNet: 99.47%, ResNet-18: 97.61%, and DenseNet-121: 98.93%; all predicted correctly. Med.121 (2020). Activation function is omitted for brevity. The distribution of images in the two datasets is provided in Table 3. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. 2a).The case was discussed at the thoracic tumor board, and a malignancy was highly suspected. Source code of our CADx system is available as open source for COVID-19 research. Internet Explorer). This strategy is implemented to ensure that the test set remains independent for predictions. Fig 13 shows two test samples from the Kermany dataset [4] where two base learners yielded incorrect predictions with a low confidence rate and the third base learner yielded the correct prediction with a very high confidence rate, finally leading the ensemble framework to predict the sample correctly. A noncancerous condition causes the abnormal growth. [20] devised simple CNN architectures for the classification of pneumonic chest X-ray images. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection. Sometimes a skin lump can look a lot like a cancer. An official website of the United States government. Jaiswal et al. If you have a pulmonary nodule, your healthcare provider may want to perform additional tests to determine the cause and rule out lung cancer. https://www.kaggle.com/c/rsna-pneumonia-detection-challenge. (II) The RSNA Pneumonia Detection Challenge dataset available on Kaggle contains CXR images of non-COVID-19 pneumonia and the healthy17. Sharma H., Jain J., Bansal P. & Gupta S. Feature extraction and classification of chest x-ray images using cnn to detect pneumonia. Received 2021 Jun 19; Accepted 2021 Aug 12. If lung cancer is present, chest X-rays can sometimes detect larger tumors. First, we developed and validated the proposed method using the public datasets. Rajinikanth V., Kadry S., Damaeviius R., Taniar D. & Rauf H. Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image. B. The Journal of Pediatrics The cause and clinical manifestations of pneumonia were studied in 98 pediatric outpatients. Mach. Other causes of noncancerous lung nodules include: Anyone can develop pulmonary nodules. If a nodule is cancerous, your healthcare provider can discuss next steps. Lung cancer and pneumonia both cause the same symptoms and signs that may include: Cough Chest pain and/or discomfort Shortness of breath It should be noted that the proposed method outperformed all the other methods. For EfficientNet, the best model was selected from B0B7 by the random search. Went on antibiotic, had chest X-ray. Policy. An inception block accommodates a large number of units at each stage by hosting parallel convolution and pooling layers, resulting in an uncontrolled computational complexity because of the increased number of parameters. Data curation: M.N. Supervision: T.M. Get useful, helpful and relevant health + wellness information, 9500 Euclid Avenue, Cleveland, Ohio 44195 |, Important Updates + Notice of Vendor Data Event, (https://www.cancer.org/cancer/lung-cancer.html), (https://www.lung.org/lung-health-diseases/lung-procedures-and-tests), (https://www.lung.org/lung-health-diseases/warning-signs-of-lung-disease), (https://www.thoracic.org/patients/patient-resources/resources/lung-nodules-online.pdf), (https://www.merckmanuals.com/home/lung-and-airway-disorders/tumors-of-the-lungs/overview-of-lung-tumors), (https://radiopaedia.org/articles/coin-lesion-lung). Bone cancer. In this section, we report the evaluation results of the proposed method. The mean sensitivity of COVID-19 pneumonia was 90.9% for the VGG16-based model. Table 6 shows the results of the various ensembles consisting of three different base learners (including recently proposed architectures), GoogLeNet, ResNet-18, ResNet-50, ResNet-152, DenseNet-121, DenseNet-169, DenseNet-201, MobileNet v2, and NasMobileNet, on the Kermany dataset. The 1248 CXR images were divided into 998, 125, and 125 images for training, validation, test sets, respectively. On the chest x-ray there is an ill-defined area of increased density in the right upper lobe without volume loss. Each year, an estimated three million Americans get pneumonia, and about 50,000 die from their disease. The main contributions of this study are as follows. If left untreated, complications can be serious, even fatal. Article Fig 10 shows the accuracy rates achieved by the base learners in transfer learning using different optimizers on the Kermany dataset. Project administration: M.N. Hence, for this dataset, we compared the performance of the proposed model to that of several baseline CNN models. 2016-December, 770778 (IEEE Computer Society, 2016). Some people have a series of chest X-rays done over time to track whether a health problem is getting better or worse. [11], Ibrahim et al. When you have an x-ray, you may wear a lead apron to protect certain parts of your body. Single type or no data augmentation methods were also evaluated. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. In 3rd International Conference on Learning Representations, ICLR 2015Conference Track Proceedings (International Conference on Learning Representations, ICLR, 2015). The probability of the dropout layer was 0.1, and the number of units in the first fully-connected layer was 416. Most approaches in the literature set the weights experimentally or based solely on the accuracy of the classifier. Rahman T., Chowdhury M., Khandakar A., Islam K., Islam K., Mahbub Z., et al. Table S1 of Supplementary information shows the effect of RICAP obtained by the random search. Lal S., Rehman S., Shah J., Meraj T., Rauf H., Damaeviius R., et al. However, this may not be a good measure when a class imbalance exists in the dataset. The best results for the ensemble were achieved when all the layers were trainable (0 layers frozen) on both datasets. In this study, we designed an ensemble framework of three classifiers (Fig 2), GoogLeNet [34], ResNet-18 [35], and DenseNet-121 [36], using a weighted average ensemble scheme wherein the weights allocated to the classifiers are generated using a novel scheme, as explained in detail in the following sections. It introduces a linear combination to the FM because only the features that have a positive influence on the respective class are of interest; the negative pixels in the image that belong to other categories are discarded. Because fewer channels are accommodated in the convolutional layers, the number of trainable parameters is diminished, and thus, the model is computationally efficient. Albahli S., Rauf H., Algosaibi A. Chronic obstructive pulmonary disease (COPD) is a serious lung disease that includes a few different breathing conditions. Yamashita, R., Nishio, M., Do, R. K. G. & Togashi, K. Convolutional neural networks: an overview and application in radiology. This mass will look like a white spot on your lungs, while the lung itself will appear black. In addition, the sensitivity of COVID-19 was more than 90%.
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