Abstract:Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.
“…3, the algorithm’s decision [36] for lung boundary (red contour) is significantly different from the expected lung anatomy (green contour delineated by an expert). The missing parts may contain important clues about the abnormality and could be useful for algorithm’s decision [5]. Therefore, automated lung boundary detection algorithms that are robust to cardiopulmonary deformities in thoracic cavity remains a challenging task.Fig.…”
Section: Lungs With Deformed Appearancementioning
confidence: 99%
“…In [5], researchers extract low-level shape features and combine them with texture features to increase the TB detection performance. The area under the curve (ROC) in detecting TB increased by 2.4% with shape features addition.…”
Section: Radiographic Measures: Radiological Signs For Pulmonary Abnomentioning
confidence: 99%
“…For instance, in study [5], two radiologists delineate the lung boundaries on the same CXRs. The inter-observer agreement is measured from the delineations.…”
PurposeChest radiography is the most common imaging modality for pulmonary diseases. Due to its wide usage, there is a rich literature addressing automated detection of cardiopulmonary diseases in digital chest X-rays (CXRs). One of the essential steps for automated analysis of CXRs is localizing the relevant region of interest, i.e., isolating lung region from other less relevant parts, for applying decision-making algorithms there. This article provides an overview of the recent literature on lung boundary detection in CXR images.MethodsWe review the leading lung segmentation algorithms proposed in period 2006–2017. First, we present a review of articles for posterior–anterior view CXRs. Then, we mention studies which operate on lateral views. We pay particular attention to works that focus their efforts on deformed lungs and pediatric cases. We also highlight the radiographic measures extracted from lung boundary and their use in automatically detecting cardiopulmonary abnormalities. Finally, we identify challenges in dataset curation and expert delineation process, and we listed publicly available CXR datasets.Results(1) We classified algorithms into four categories: rule-based, pixel classification-based, model-based, hybrid, and deep learning-based algorithms. Based on the reviewed articles, hybrid methods and deep learning-based methods surpass the algorithms in other classes and have segmentation performance as good as inter-observer performance. However, they require long training process and pose high computational complexity. (2) We found that most of the algorithms in the literature are evaluated on posterior–anterior view adult CXRs with a healthy lung anatomy appearance without considering challenges in abnormal CXRs. (3) We also found that there are limited studies for pediatric CXRs. The lung appearance in pediatrics, especially in infant cases, deviates from adult lung appearance due to the pediatric development stages. Moreover, pediatric CXRs are noisier than adult CXRs due to interference by other objects, such as someone holding the child’s arms or the child’s body, and irregular body pose. Therefore, lung boundary detection algorithms developed on adult CXRs may not perform accurately in pediatric cases and need additional constraints suitable for pediatric CXR imaging characteristics. (4) We have also stated that one of the main challenges in medical image analysis is accessing the suitable datasets. We listed benchmark CXR datasets for developing and evaluating the lung boundary algorithms. However, the number of CXR images with reference boundaries is limited due to the cumbersome but necessary process of expert boundary delineation.ConclusionsA reliable computer-aided diagnosis system would need to support a greater variety of lung and background appearance. To our knowledge, algorithms in the literature are evaluated on posterior–anterior view adult CXRs with a healthy lung anatomy appearance, without considering ambiguous lung silhouettes due to pathological deformities, anatomical alte...
“…3, the algorithm’s decision [36] for lung boundary (red contour) is significantly different from the expected lung anatomy (green contour delineated by an expert). The missing parts may contain important clues about the abnormality and could be useful for algorithm’s decision [5]. Therefore, automated lung boundary detection algorithms that are robust to cardiopulmonary deformities in thoracic cavity remains a challenging task.Fig.…”
Section: Lungs With Deformed Appearancementioning
confidence: 99%
“…In [5], researchers extract low-level shape features and combine them with texture features to increase the TB detection performance. The area under the curve (ROC) in detecting TB increased by 2.4% with shape features addition.…”
Section: Radiographic Measures: Radiological Signs For Pulmonary Abnomentioning
confidence: 99%
“…For instance, in study [5], two radiologists delineate the lung boundaries on the same CXRs. The inter-observer agreement is measured from the delineations.…”
PurposeChest radiography is the most common imaging modality for pulmonary diseases. Due to its wide usage, there is a rich literature addressing automated detection of cardiopulmonary diseases in digital chest X-rays (CXRs). One of the essential steps for automated analysis of CXRs is localizing the relevant region of interest, i.e., isolating lung region from other less relevant parts, for applying decision-making algorithms there. This article provides an overview of the recent literature on lung boundary detection in CXR images.MethodsWe review the leading lung segmentation algorithms proposed in period 2006–2017. First, we present a review of articles for posterior–anterior view CXRs. Then, we mention studies which operate on lateral views. We pay particular attention to works that focus their efforts on deformed lungs and pediatric cases. We also highlight the radiographic measures extracted from lung boundary and their use in automatically detecting cardiopulmonary abnormalities. Finally, we identify challenges in dataset curation and expert delineation process, and we listed publicly available CXR datasets.Results(1) We classified algorithms into four categories: rule-based, pixel classification-based, model-based, hybrid, and deep learning-based algorithms. Based on the reviewed articles, hybrid methods and deep learning-based methods surpass the algorithms in other classes and have segmentation performance as good as inter-observer performance. However, they require long training process and pose high computational complexity. (2) We found that most of the algorithms in the literature are evaluated on posterior–anterior view adult CXRs with a healthy lung anatomy appearance without considering challenges in abnormal CXRs. (3) We also found that there are limited studies for pediatric CXRs. The lung appearance in pediatrics, especially in infant cases, deviates from adult lung appearance due to the pediatric development stages. Moreover, pediatric CXRs are noisier than adult CXRs due to interference by other objects, such as someone holding the child’s arms or the child’s body, and irregular body pose. Therefore, lung boundary detection algorithms developed on adult CXRs may not perform accurately in pediatric cases and need additional constraints suitable for pediatric CXR imaging characteristics. (4) We have also stated that one of the main challenges in medical image analysis is accessing the suitable datasets. We listed benchmark CXR datasets for developing and evaluating the lung boundary algorithms. However, the number of CXR images with reference boundaries is limited due to the cumbersome but necessary process of expert boundary delineation.ConclusionsA reliable computer-aided diagnosis system would need to support a greater variety of lung and background appearance. To our knowledge, algorithms in the literature are evaluated on posterior–anterior view adult CXRs with a healthy lung anatomy appearance, without considering ambiguous lung silhouettes due to pathological deformities, anatomical alte...
“…They combine elements of computer vision and artificial intelligence with radiological image processing for recognizing patterns [4]. Much of the published literature describes machine learning (ML) algorithms that use handcrafted feature descriptors [5] that are optimized for individual datasets and trained for specific variability in size, orientation, and position of the region of interest (ROI) [6]. In recent years, data-driven deep learning (DL) methods are shown to avoid the issues with handcrafted features through end-to-end feature extraction and classification.…”
Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network (CNN) extracted image features for visual recognition. However, CNNs are perceived as black boxes since their performance lack explanations. This is a serious bottleneck in applications involving medical screening/diagnosis since poorly interpreted model behavior could adversely affect the clinical decision. In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. We present a novel visualization strategy to localize the region of interest (ROI) that is considered relevant for model predictions across all the inputs that belong to an expected class. We statistically validate the models' performance toward the underlying tasks. We observe that the customized VGG16 model achieves 96.2% and 93.6% accuracy in detecting the disease and distinguishing between bacterial and viral pneumonia respectively. The model outperforms the state-of-the-art in all performance metrics and demonstrates reduced bias and improved generalization. Figure 1. Pediatric CXRs: (a) Normal CXR showing clear lungs with no abnormal opacification; (b) Bacterial pneumonia exhibiting focal lobar consolidation in the right upper lobe; (c) Viral pneumonia manifesting with diffuse interstitial patterns in both lungs.Computer-aided diagnostic (CADx) tools aim to supplement clinical decision-making. They combine elements of computer vision and artificial intelligence with radiological image processing for recognizing patterns [4]. Much of the published literature describes machine learning (ML) algorithms that use handcrafted feature descriptors [5] that are optimized for individual datasets and trained for specific variability in size, orientation, and position of the region of interest (ROI) [6]. In recent years, data-driven deep learning (DL) methods are shown to avoid the issues with handcrafted features through end-to-end feature extraction and classification.Convolutional neural networks (CNNs) belong to a class of DL models that are prominently used in computer vision [7]. These models have multiple processing layers to learn hierarchical feature representations from the input pixel data. The features in the early network layers are abstracted through the mechanisms of local receptive fields, weight sharing, and pooling to form rich feature representations toward learning and classifying the inputs to their respective classes. Due to lack of sufficiently extensive medical image data, CNNs trained on large-scale data collections such as ImageNet [8] are used to transfer the knowledge of learned representations in the form of generic image features to the current task. CNNs are also shown to deliver promising...
“…TP indicates true positive (True Positive) (i.e., predicted to suffer from lung disease and actually suffering from lung disease), while TN is true negative as predicted to suffer from lung disease and actually suffering from lung disease, while TN is true negative (True Negative) (i.e., the predicted absence of lung disease and no recorded presence of lung disease). FP is a false positive, which predicts the development of lung disease that is not actually present, while FN is a false negative which predicts no development of lung disease despite the real presence of lung disease, the Formulas (4)-(7) based on the work in [36][37][38][39].…”
Section: Evaluating Cnn Model Performance For Lung Disease Predictionmentioning
Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.
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