81 research outputs found
Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images
The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed
the world. According to the World Health Organization (WHO), there have been
more than 100 million confirmed cases of COVID-19, including more than 2.4
million deaths. It is extremely important the early detection of the disease,
and the use of medical imaging such as chest X-ray (CXR) and chest Computed
Tomography (CCT) have proved to be an excellent solution. However, this process
requires clinicians to do it within a manual and time-consuming task, which is
not ideal when trying to speed up the diagnosis. In this work, we propose an
ensemble classifier based on probabilistic Support Vector Machine (SVM) in
order to identify pneumonia patterns while providing information about the
reliability of the classification. Specifically, each CCT scan is divided into
cubic patches and features contained in each one of them are extracted by
applying kernel PCA. The use of base classifiers within an ensemble allows our
system to identify the pneumonia patterns regardless of their size or location.
Decisions of each individual patch are then combined into a global one
according to the reliability of each individual classification: the lower the
uncertainty, the higher the contribution. Performance is evaluated in a real
scenario, yielding an accuracy of 97.86%. The large performance obtained and
the simplicity of the system (use of deep learning in CCT images would result
in a huge computational cost) evidence the applicability of our proposal in a
real-world environment.Comment: 15 pages, 9 figure
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance
People all over the globe are affected by pneumonia but deaths due to it are
highest in Sub-Saharan Asia and South Asia. In recent years, the overall
incidence and mortality rate of pneumonia regardless of the utilization of
effective vaccines and compelling antibiotics has escalated. Thus, pneumonia
remains a disease that needs spry prevention and treatment. The widespread
prevalence of pneumonia has caused the research community to come up with a
framework that helps detect, diagnose and analyze diseases accurately and
promptly. One of the major hurdles faced by the Artificial Intelligence (AI)
research community is the lack of publicly available datasets for chest
diseases, including pneumonia . Secondly, few of the available datasets are
highly imbalanced (normal examples are over sampled, while samples with ailment
are in severe minority) making the problem even more challenging. In this
article we present a novel framework for the detection of pneumonia. The
novelty of the proposed methodology lies in the tackling of class imbalance
problem. The Generative Adversarial Network (GAN), specifically a combination
of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein
GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia''
for augmentation, whereas Random Under-Sampling (RUS) was done on the majority
class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8
dataset, one of the biggest datasets, is used to validate the performance of
the proposed framework. The learning phase is completed using transfer learning
on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16.
Results obtained exceed state-of-the-art
Improving Pneumonia Classification and Lesion Detection Using Spatial Attention Superposition and Multilayer Feature Fusion
Pneumonia is a severe inflammation of the lung that could cause serious complications. Chest X-rays (CXRs) are commonly used to make a diagnosis of pneumonia. In this paper, we propose a deep-learning-based method with spatial attention superposition (SAS) and multilayer feature fusion (MFF) to facilitate pneumonia diagnosis based on CXRs. Specifically, an SAS module, which takes advantage of the channel and spatial attention mechanisms, was designed to identify intrinsic imaging features of pneumonia-related lesions and their locations, and an MFF module was designed to harmonize disparate features from different channels and emphasize important information. These two modules were concatenated to extract critical image features serving as the basis for pneumonia diagnosis. We further embedded the proposed modules into a baseline neural network and developed a model called SAS-MFF-YOLO to diagnose pneumonia. To validate the effectiveness of our model, extensive experiments were conducted on two CXR datasets provided by the Radiological Society of North America (RSNA) and the AI Research Institute. SAS-MFF-YOLO achieved a precision of 88.1%, a recall of 98.2% for pneumonia classification and an AP50 of 99% for lesion detection on the AI Research Institute dataset. The visualization of intermediate feature maps showed that our method could facilitate uncovering pneumonia-related lesions in CXRs. Our results demonstrated that our approach could be used to enhance the performance of the overall pneumonia detection on CXR imaging
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, âArtificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseasesâ, 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
The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review
Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided
Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey
Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table
Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques
Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease
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