76 research outputs found
Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases
Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI
Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach
Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Model initialization techniques are vital for improving the performance and
reliability of deep learning models in medical computer vision applications.
While much literature exists on non-medical images, the impacts on medical
images, particularly chest X-rays (CXRs) are less understood. Addressing this
gap, our study explores three deep model initialization techniques: Cold-start,
Warm-start, and Shrink and Perturb start, focusing on adult and pediatric
populations. We specifically focus on scenarios with periodically arriving data
for training, thereby embracing the real-world scenarios of ongoing data influx
and the need for model updates. We evaluate these models for generalizability
against external adult and pediatric CXR datasets. We also propose novel
ensemble methods: F-score-weighted Sequential Least-Squares Quadratic
Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy
Softmax to aggregate weight parameters from multiple models to capitalize on
their collective knowledge and complementary representations. We perform
statistical significance tests with 95% confidence intervals and p-values to
analyze model performance. Our evaluations indicate models initialized with
ImageNet-pre-trained weights demonstrate superior generalizability over
randomly initialized counterparts, contradicting some findings for non-medical
images. Notably, ImageNet-pretrained models exhibit consistent performance
during internal and external testing across different training scenarios.
Weight-level ensembles of these models show significantly higher recall
(p<0.05) during testing compared to individual models. Thus, our study
accentuates the benefits of ImageNet-pretrained weight initialization,
especially when used with weight-level ensembles, for creating robust and
generalizable deep learning solutions.Comment: 40 pages, 8 tables, 7 figures, 3 supplementary figures and 4
supplementary table
A bone suppression model ensemble to improve COVID-19 detection in chest X-rays
Chest X-ray (CXR) is a widely performed radiology examination that helps to
detect abnormalities in the tissues and organs in the thoracic cavity.
Detecting pulmonary abnormalities like COVID-19 may become difficult due to
that they are obscured by the presence of bony structures like the ribs and the
clavicles, thereby resulting in screening/diagnostic misinterpretations.
Automated bone suppression methods would help suppress these bony structures
and increase soft tissue visibility. In this study, we propose to build an
ensemble of convolutional neural network models to suppress bones in frontal
CXRs, improve classification performance, and reduce interpretation errors
related to COVID-19 detection. The ensemble is constructed by (i) measuring the
multi-scale structural similarity index (MS-SSIM) score between the sub-blocks
of the bone-suppressed image predicted by each of the top-3 performing
bone-suppression models and the corresponding sub-blocks of its respective
ground truth soft-tissue image, and (ii) performing a majority voting of the
MS-SSIM score computed in each sub-block to identify the sub-block with the
maximum MS-SSIM score and use it in constructing the final bone-suppressed
image. We empirically determine the sub-block size that delivers superior bone
suppression performance. It is observed that the bone suppression model
ensemble outperformed the individual models in terms of MS-SSIM and other
metrics. A CXR modality-specific classification model is retrained and
evaluated on the non-bone-suppressed and bone-suppressed images to classify
them as showing normal lungs or other COVID-19-like manifestations. We observed
that the bone-suppressed model training significantly outperformed the model
trained on non-bone-suppressed images toward detecting COVID-19 manifestations.Comment: 29 pages, 10 figures, 4 table
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Chest radiographs are primarily employed for the screening of pulmonary and
cardio-/thoracic conditions. Being undertaken at primary healthcare centers,
they require the presence of an on-premise reporting Radiologist, which is a
challenge in low and middle income countries. This has inspired the development
of machine learning based automation of the screening process. While recent
efforts demonstrate a performance benchmark using an ensemble of deep
convolutional neural networks (CNN), our systematic search over multiple
standard CNN architectures identified single candidate CNN models whose
classification performances were found to be at par with ensembles. Over 63
experiments spanning 400 hours, executed on a 11:3 FP32 TensorTFLOPS compute
system, we found the Xception and ResNet-18 architectures to be consistent
performers in identifying co-existing disease conditions with an average AUC of
0.87 across nine pathologies. We conclude on the reliability of the models by
assessing their saliency maps generated using the randomized input sampling for
explanation (RISE) method and qualitatively validating them against manual
annotations locally sourced from an experienced Radiologist. We also draw a
critical note on the limitations of the publicly available CheXpert dataset
primarily on account of disparity in class distribution in training vs. testing
sets, and unavailability of sufficient samples for few classes, which hampers
quantitative reporting due to sample insufficiency.Comment: accepted in EMBC 2020, 4 pages+2 page Appendi
Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.
Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading
causes of mortality in developing countries. This is due to poverty and inadequate
medical resources. While treatment for TB is possible, it requires an accurate diagnosis
first. Several screening tools are available, and the most reliable is Chest
X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR
images is often lacking. Over the years, CXR has been manually examined; this
process results in delayed diagnosis, is time-consuming, expensive, and is prone
to misdiagnosis, which could further spread the disease among individuals. Consequently,
an algorithm could increase diagnosis efficiency, improve performance,
reduce the cost of manual screening and ultimately result in early/timely diagnosis.
Several algorithms have been implemented to diagnose TB automatically. However,
these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis.
In recent years, Convolutional Neural Networks (CNN), a class of Deep
Learning, has demonstrated tremendous success in object detection and image classification
task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis
(CAD) system with high accuracy and sensitivity for TB detection and classification.
The proposed model is based firstly on novel end-to-end CNN architecture,
then a pre-trained Deep CNN model that is fine-tuned and employed as a features
extractor from CXR. Finally, Ensemble Learning was explored to develop an
Ensemble model for TB classification. The Ensemble model achieved a new stateof-
the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity
and 0.96% AUC. These results are comparable with state-of-the-art techniques and
outperform existing TB classification models.Author's Publications listed on page iii
A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications
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