Conference Paper

Two-Stage Deep Learning Architecture for Pneumonia Detection and its Diagnosis in Chest Radiographs

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Abstract

Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such as AlexNet, ResNet, VGG16 and Inception-v3 for detection of pneumonia. For these networks, we employ transfer learning to exploit the wealth of information available from prior training. For the second stage, we find that transfer learning with these same networks tends to overfit the data. For this reason we propose a simpler CNN architecture for classification of pneumonic chest radiographs and show that it overcomes the overfitting problem. We further enhance the performance of our system in a novel way by incorporating lung segmentation using a U-Net architecture. We make use of a publicly available dataset comprising 5856 images (1583 – Normal, 4273 – Pneumonic). Among the pneumonia patients, 2780 patients are identified as bacteria type and the rest belongs to virus category. We test our proposed algorithm(s) on a set of 624 images and we achieve an area under the receiver operating characteristic curve of 0.996 for pneumonia detection. We also achieve an accuracy of 97.8% for classification of pneumonic chest radiographs thereby setting a new benchmark for both detection and diagnosis. We believe the proposed two-stage classification of chest radiographs for pneumonia detection and its diagnosis would enhance the workflow of radiologists.

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... A U-Net architecture is presented for automated lung segmentation in chest radiographs in [9]. In [10], a novel two-stage architecture to detect and diagnose pneumonia is presented using transfer learning approaches. Independent architectures are used for pneumonia detection and diagnosis. ...
... Independent architectures are used for pneumonia detection and diagnosis. Detection of pneumonia is implemented using established transfer learning approaches [10] and later lung regions are segmented using U-Net architecture before passing it to classification architecture for diagnosing pneumonia patients as bacterial or viral. A CAD tool based on wavelet transforms is presented to detect pneumonia in [11]. ...
... Recent research work clearly indicates that deep learning has proven to be highly effective for CAD tools in chest radiographs [3,9,10,[15][16][17][18]. However, balanced sets of chest radiographs with COVID-19 markings are available in limited quantity, making it a difficult problem to address using traditional deep learning approaches. ...
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The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and apply transfer learning between these. We demonstrate the efficacy of the method using well-established deep convolutional neural networks. Our proposed training mechanism is more robust to limited training data and class imbalance. We study the performance of our algorithm(s) based on 10-fold cross validation and two hold-out validation experiments to demonstrate its efficacy. We achieved an overall sensitivity of 0.94 for the hold-out validation experiments containing 2265 and 2139 marked as COVID-19 chest radiographs, respectively. For the 10-fold cross validation experiment, we achieve an overall Area under the Receiver Operating Characteristic curve (AUC) value of 0.996 for COVID-19 detection. This paper serves as a proof-of-concept that an automated detection approach can be developed with a limited set of COVID-19 images, and in areas with scarcity of trained radiologists.
... Residual Neural network architecture was adopted through our work using a ResNet50 deeper network with 50 layers. This network gave a significant performance in various medical imaging applications [26,27]. Its architecture achieved an optimal tradeoff between a speed and efficiency. ...
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Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lym-phoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types are Classical Hodgkin Lymphoma, Nodular Lymphoma Predominant , Burkitt Lymphoma, Follicular Lymphoma, Mantle Lymphoma, Large B-Cell Lymphoma, and T-Cell Lymphoma. Our proposed approach uses Residual Neural Networks, ResNet50, with a Transfer Learning for lymphoma's detection and classification. The model used results are validated according to the performance evaluation metrics: Accuracy, precision, recall, F-score, and kappa score for the seven multi-classes. Our algorithms are tested, and the results are validated on 323 images of 224 × 224 pixels resolution. The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.
... Datasets: BL=Belarus, C=ChestX-ray14, CC=COVID-CXR, CG=COVIDGR, J=JSRT+SCR, M=MIMIC-CXR, MO=Montgomery, O=Open-i, P=PadChest, PL=PLCO, PP=Ped-pneumonia, PR=Private, RP=RSNA-Pneumonia, S=Shenzen, SI=SIIM-ACR, SM=Simulated CXR from CT, X=CheXpert. Introduces a visualization method to identify regions of interest from classification LC TB PR Hwang and Kim (2016) Weakly supervised framework jointly trained with localization and classification LC TB PR Wang et al. (2018) Combines classification loss and autoencoder reconstruction loss IG,SE T J,MO,O,S Seah et al. (2019) Wasserstein GAN to permute diseased radiographs to appear healthy IG,LC Z PR Wolleb et al. (2020) Novel GAN model trained with healthy and abnormal CXR to predict difference map IG PE SM,X Tang et al. (2019c)GANs with U-Net autoencoder and CNN discriminator and encoder for one-class learning IG T CMao et al. (2020) Autoencoder uses uncertainty for reconstruction error in one-class learning setting IG T PP,RPLenga et al. (2020) Continual learning methods to classify data from new domains DA C,M C,M Tang et al. (2019a) CycleGAN model to adapt adult to pediatric CXR for pneumonia classification CC,PR,RPKusakunniran et al. (2021) ResNet-101 trained for COVID-19, heatmaps are generated for lung-segmented regions SE CV,PM PRNarayanan et al. (2020) Multiple architectures considered for two-stage classification of pediatric pneumonia SE PM PPRajaraman et al. (2019b) Compares visualization methods for pneumonia localization SE PM PPBlumenfeld et al. (2018) Classifies patches and uses the positive area size to classify the image SE PT PRRajaraman et al. (2018b) Feature extraction from CNN models and ensembling methods SE TB MO,PR,S Subramanian et al.(2019)Detection of central venous catheters using segmentation shape analysis SE TU CMansoor et al. (2016) Detection of air-trapping in pediatric CXRs using Stacked Autoencoders SE Z PRWang et al. (2020e) Pneumoconiosis detection using Inception-v3 and evaluation against two radiologists SE Z PRIrvin et al. (2019) Introduces CheXpert dataset and model performance on radiologist labeled test set ...
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... A generic approach to lung field segmentation from chest radiographs using deep space and shape learning [31] S2 A novel transfer learning based approach for pneumonia detection in chest X-ray images [32] S3 A transfer learning method with deep residual network for pediatric pneumonia diagnosis [33] S4 An efficient deep learning approach to pneumonia classification in healthcare [34] S5 Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study [35] S6 Automated pneumonia diagnosis using a customized sequential convolutional neural network [36] S7 Automatic catheter and tube detection in pediatric x-ray images using a scale-recurrent network and synthetic data [37] S8 Automatic tissue characterization of air trapping in chest radiographs using deep neural networks [38] S9 Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography [39] S10 Classification of images of childhood pneumonia using convolutional neural networks [40] S11 Classification of pneumonia from x-ray images using siamese convolutional network [41] S12 Deep learning method for automated classification of anteroposterior and posteroanterior chest radiographs [42] S13 Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis [43] S14 Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images [44] S15 Detecting pneumonia in chest radiographs using convolutional neural networks [45] S16 Detection of pediatric pneumonia from chest x-Ray images using CNN and transfer learning [46] S17 Discriminant analysis deep neural networks [47] S18 Identifying medical diagnoses and treatable diseases by image-based deep learning [48] S19 Learning to recognize chest-xray images faster and more efficiently based on multi-kernel depthwise convolution [49] S20 LungAIR: An automated technique to predict hospitalization due to LRTI using fused information [50] S21 Marginal shape deep learning: Applications to pediatric lung field segmentation [51] S22 Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks [52] S23 Simultaneous lung field detection and segmentation for pediatric chest radiographs [53] S24 Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs [54] S25 Using deep-learning techniques for pulmonary-thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs [55] S26 Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs [56] TABLE IV: Selected papers and digital libraries STUDY S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 ...
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Our objective was to evaluate the impact of a computer-aided diagnostic scheme on radiologists' interpretations of chest radiographs with interstitial opacities by performing an observer test using receiver operating characteristic (ROC) analysis. Twenty chest radiographs with normal findings and 20 chest radiographs with abnormal findings were used. Each radiograph was divided into four quadrants. One hundred twenty-nine quadrants (80 normal and 49 abnormal quadrants) were used for testing because we excluded 31 equivocal quadrants. Sixteen independent observers (10 residents and six attending radiologists) participated in this study. The radiologists' performance without and with computer assistance, which indicated cases with normal and abnormal findings by various markers, was evaluated by ROC analysis. The diagnostic accuracy of the observers improved by a statistically significant magnitude when computer-aided diagnosis was used. Thus, the values for the area under the ROC curve obtained with and without the computer-aided diagnostic output were .970 and .948 (p = .0002), respectively, for all observers; .969 and .943 (p = .0006), respectively, for the residents' subgroup; and .972 and .960 (p = .162), respectively, for the attending radiologists' subgroup. The value for the area under the ROC curve for the computerized scheme by itself was .943. Our computer-aided diagnostic scheme can assist radiologists in the diagnosis or exclusion of interstitial disease on chest radiographs.
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This article presents a novel approach based on computer-aided diagnostic (CAD) scheme and wavelet transforms to aid pneumonia diagnosis in children, using chest radiograph images. The prototype system, named Pneumo-CAD, was designed to classify images into presence (PP) or absence of pneumonia (PA). The knowledge database for the Pneumo-CAD comprised chest images confirmed as PP or PA by two radiologists trained to interpret chest radiographs according to the WHO guidelines for the diagnosis of pneumonia in children. The performance of the Pneumo-CAD was evaluated by a subset of images randomly selected from the knowledge database. The retrieval of similar images was made by feature extraction using wavelets transform coefficients of the image. The energy of the wavelet coefficients was used to compose the feature vector in order to support the computational classification of images as PP or PA. Methodology I worked with a rank-weighted 15-nearest-neighbour scheme, while methodology II employed a distance-dependent weighting for image classification. The performance of the prototype system was assessed by the ROC curve. Overall, the Pneumo-CAD using the Haar wavelet presented the best accuracy in discriminating PP from PA for both, methodology I (AUC=0.97) and methodology II (AUC=0.94), reaching sensitivity of 100% and specificity of 80% and 90%, respectively. Pneumo-CAD could represent a complementary tool to screen children with clinical suspicion of pneumonia, and so to contribute to gather information on the burden of-pneumonia estimates in order to help guide health policies toward preventive interventions.
Article
A new computer aided detection (CAD) system is presented for the detection of pulmonary nodules on chest radiographs. Here, we present the details of the proposed algorithm and provide a performance analysis using a publicly available database to serve as a benchmark for future research efforts. All aspects of algorithm training were done using an independent dataset containing 167 chest radiographs with a total of 181 lung nodules. The publicly available test set was created by the Standard Digital Image Database Project Team of the Scientific Committee of the Japanese Society of Radiological Technology (JRST). The JRST dataset used here is comprised of 154 chest radiographs containing one radiologist confirmed nodule each (100 malignant cases, 54 benign cases). The CAD system uses an active shape model for anatomical segmentation. This is followed by a new weighted-multiscale convergence-index nodule candidate detector. A novel candidate segmentation algorithm is proposed that uses an adaptive distance-based threshold. A set of 114 features is computed for each candidate. A Fisher linear discriminant (FLD) classifier is used on a subset of 46 features to produce the final detections. Our results indicate that the system is able to detect 78.1% of the nodules in the JRST test set with and average of 4.0 false positives per image (excluding 14 cases containing lung nodules in retrocardiac and subdiaphragmatic regions of the lung).
Variable N-Quoit filter applied for automatic detection of lung cancer by X-ray CT
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Performance analysis of machine learning and deep learning architectures for malaria detection on cell images
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