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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... In the medical domain, image segmentation has been made the image analysis easier [16]. In a related study, Narayanan et al. [62] have used transfer learning with AlexNet, ResNet, VGG-16 and InceptionV3 models to detect pneumonia conditions using chest X-ray images. Their approach has shown a high performance with the InceptionV3 model with 99% of training accuracy and 98.9% of validation accuracy. ...
... The U-Net model based on EfficientNet-B4 has achieved the highest accuracy, precision, and F1-score whereas the ensemble of the two models has achieved the highest recall. A study conducted by Narayanan et al. [62], has applied a U-Net architecture to implement a lung segmentation algorithm to enhance their model performance. Further, Rahman et al. [99], have presented an architecture adapted from U-Net to generate lung field segmentation. ...
... Virus and Bacterial Pneumonia [9,40,57,58,62,68,75,78,80,81,105,106,[113][114][115][116] Kaggle COVID-19 Patients Lungs X Ray Images 10000 [117] 100 COVID-19 [42] Chest X-ray14 (latest version of chest X-ray8) [118] 112,120 Pneumonia Pathology classes [23,70,76,81,85,87,92,93,119] CheXpert [120] 224,316 Pneumonia [23,57] COVID-19 X rays [121] 95 COVID-19 [10,59] COVIDx [44] 13,975 Bacterial and Viral Pneumonia, COVID-19 [44,47,72,79,89] CoronaHack -Chest X-ray-Dataset [122] 5933 COVID-19 [56] Mendeley Augmented COVID-19 X-ray Images Dataset [123] 1824 COVID-19 [67] Evolution ( Table 6 shows that most of the related studies have used existing DL architectures instead of CNN built from scratch. For instance, 44% of the studies indicated in Table 6 have developed their own CNN architecture, while the rest have used existing DL architectures. ...
Article
Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyse chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.
... 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.
... The researcher used three pre-trained models to achieve the best accuracy and develop a deep comparative analysis for the best detection technique. The researchers (Narayanan et al., 2020) have worked on a two-stage deep learning model to detect the presence of viral pneumonia by using chest radiography and incorporated it with image segmentation techniques to achieve better results. Early diagnosis is necessary to detect the presence of viral or bacterial infection in the lungs Al Mamlook et al. (2020) conducted a comparative analysis with the previous studies to signify the use of deep learning compared with machine learning approaches. ...
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Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep learning is extensively applied in the realm of medicine and disease classification. Early diagnosis of pneumonia is essential so it can be efficiently treated with the type of antibiotics. Bacterium and viruses are the population's first cause of pneumonia and death. Bacteria and viruses are part of mammalian pathogens and the most invasive type of bacteria or virus causing many diseases. Bacterial infection is among the most common types of disease in all age groups, but most bacterial infectious diseases are not the same. Our research will propose a transfer learning-based approach for pneumonia prediction utilizing a dataset comprising chest X-ray images. The dataset-based images will be grouped into two groups based on the parameters. Our proposed model displayed an average accuracy of 94.54% on the dataset. The proposed model (PDTLA) performed well compared with previous quantitative and qualitative research studies. Pneumonia detection transfer learning algorithm (PDTLA) is the name of the modified model.
... The final results demonstrate that the combination of LDA and mRMR achieved the best accuracy which is 99.41%. Narayanan et al. [25] suggested a two-stage deep learning architecture for pneumonia diagnosis from chest radiographs. The second stage was more efficient and it was based on a CNN architecture proceeded by lung segmentation using U-Net architecture. ...
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Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches.
... The proposed CNN model without lung segmentation has shown 96.7% training accuracy and 96% validation accuracy. In comparison, the same CNN model with lung segmentation has achieved 98.5% training accuracy and 98.3% validation accuracy for detecting bacterial vs. viral pneumonia, showing the performance increase due to the segmentation [30]. ...
... Tang et al. (2019a) used CycleGAN to generate synthetic data and proposed TUNA-Net to adapt adult to pediatric pneumonia classification from CXRs. Narayanan et al. (2020) used UNet for lung segmentation followed by a two-level classification viz; level 1 classifies given CXR into pneumonia or normal, and level 2 further classifies pneumonia CXR into either bacterial or viral class. Rajaraman et al. (2019) highlighted different visualization techniques for interpreting CNN-based pneumonia detection using CXRs. ...
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Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.
... Using lung cropped CXR model with a CXR model to improve model performance ChestX-ray14 JSRT + SCR, 2 [73] Use of image-level prediction of Cardiomegaly and application for segmentation models ChestX-ray14 3 [74] Classification of cardiomegaly using a network with DenseNet and U-Net ChestX-ray14 4 [75] Employing lung cropped CXR model with CXR model using the segmentation quality MIMIC-CXR 5 [76] Improving Pneumonia detection by using of lung segmentation Pneumonia 6 [77] Segmentation of pneumonia using U-Net based model RSNA-Pneumonia 7 [78] To find similar studies, a database has been used for the intermediate ResNet-50 features Montgomery, Shenzen 8 [79] Detection and localization of COVID-19 through various networks and ensembling COVID 9 [80] GoogleNet has been trained with CXR patches and correlates with COVID-19 severity score ChestX-ray14 10 [81] A segmentation and classification model proposed to compare with radiologist cohort Private 11 [82] A CNN model proposed for identification of abnormal CXRs and localization of abnormalities Private 12 [83] Localizing COVID-19 opacity and severity detection on CXRs Private 13 [84] Use of Lung cropped CXR in DenseNet for cardiomegaly detection Open-I, PadChest 14 [85] Applied multiple models and combinations of CXR datasets to detect COVID-19 ChestX-ray14 JSRT + SCR, COVID-CXR 15 [86] Multiple architectures evaluated for two-stage classification of pneumonia Ped-pneumonia 16 [87] Inception-v3 based pneumoconiosis detection and evaluation against two radiologists Private 17 [88] VGG-16 architecture adapted for classification of pediatric pneumonia types Ped-pneumonia 18 [89] Used ResNet-50 as backbone for segmentation model to detect healthy, pneumonia, and COVID-19 COVID-CXR 19 [90] CNN employed to detect the presence of subphrenic free air from CXR Private 20 [91] Binary classification vs One-class identification of viral pneumonia cases Private 21 [92] Applied a weighting scheme to improve abnormality for classification ChestX-ray14 22 [93] To improve image-level classification, a Lesion detection network has been employed Private 23 [94] An ensemble scheme has been used for DenseNet-121 networks for COVID-19 classification ChestX-ray14 ...
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Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports.
... 649 Medical Imaging 2020 "Imaging Informatics for Healthcare, Research and Applications" was held in Houston, Texas, and chaired by Po-Hao Chen and Thomas M. Deserno. While papers were included on a new generation of PACS based on artificial intelligence, 652 cloud platforms for CAD and collaboration, 653 and 3D printing, 654 the overwhelming number of papers presented were on deep learning, [655][656][657][658][659][660][661] including some novel architectures: unsupervised learning, 662 3D attention U-Net, 663 and GANs. 664 ...
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... 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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... 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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Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.
... The present work mainly focuses on further improving performance of classification between COVID-19 and Non-CIVID-19. In this work, a well-known pre-trained CNN model, ResNet-50 was used [22,23]. ResNet, a short name for residual network, is a pre-trained model that has been trained on more than one million images in the ImageNet database [24] and was the winner of Im-ageNet challenge in 2015. ...
... 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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Chest radiography is still a useful examination in various situations, although CT has become a modality of choice as a diagnostic examination in many cases. Current computer-aided diagnosis (CAD) schemes for chest radiographs include nodule detection, interstitial disease detection, temporal subtraction, differential diagnosis of interstitial disease, and distinction between benign and malignant pulmonary nodules. All of these schemes are demonstrated as providing potentially useful tools for radiologists when the output of these schemes is used as a "second opinion." There are some commercially available products for these schemes and more are expected to be available in the near future. The current status of CAD for CT is also discussed briefly in this article.
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We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
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Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) automated anatomical segmentation (iii) detection of potential nodule candidates (iv) feature computation & selection and (v) candidate classification. In this paper, we study the performance of the FlyerScan by implementing various classification methods such as linear, quadratic and Fischer linear discriminant classifier. This algorithm is implemented using a publicly available Lung Image Database Consortium — Image Database Resource Initiative (LIDC-IDRI) dataset. 107 cases from LIDC-IDRI are handpicked in particular for this paper and performance of the CAD system is studied based on 5 example cases of Automatic Nodule Detection (ANODE09) database. This research will aid in improving the nodule detection rate in CT scans, thereby enhancing a patient's chance of survival.
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Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error and 17.3% top-1 error.
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In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.
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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.
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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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