204 research outputs found
A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images
Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time
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
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
A Deep Learning Study on Osteosarcoma Detection from Histological Images
In the U.S, 5-10\% of new pediatric cases of cancer are primary bone tumors.
The most common type of primary malignant bone tumor is osteosarcoma. The
intention of the present work is to improve the detection and diagnosis of
osteosarcoma using computer-aided detection (CAD) and diagnosis (CADx). Such
tools as convolutional neural networks (CNNs) can significantly decrease the
surgeon's workload and make a better prognosis of patient conditions. CNNs need
to be trained on a large amount of data in order to achieve a more trustworthy
performance. In this study, transfer learning techniques, pre-trained CNNs, are
adapted to a public dataset on osteosarcoma histological images to detect
necrotic images from non-necrotic and healthy tissues. First, the dataset was
preprocessed, and different classifications are applied. Then, Transfer
learning models including VGG19 and Inception V3 are used and trained on Whole
Slide Images (WSI) with no patches, to improve the accuracy of the outputs.
Finally, the models are applied to different classification problems, including
binary and multi-class classifiers. Experimental results show that the accuracy
of the VGG19 has the highest, 96\%, performance amongst all binary classes and
multiclass classification. Our fine-tuned model demonstrates state-of-the-art
performance on detecting malignancy of Osteosarcoma based on histologic images
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
Can AI help in screening Viral and COVID-19 pneumonia?
Coronavirus disease (COVID-19) is a pandemic disease, which has already
caused thousands of causalities and infected several millions of people
worldwide. Any technological tool enabling rapid screening of the COVID-19
infection with high accuracy can be crucially helpful to healthcare
professionals. The main clinical tool currently in use for the diagnosis of
COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which
is expensive, less-sensitive and requires specialized medical personnel. X-ray
imaging is an easily accessible tool that can be an excellent alternative in
the COVID-19 diagnosis. This research was taken to investigate the utility of
artificial intelligence (AI) in the rapid and accurate detection of COVID-19
from chest X-ray images. The aim of this paper is to propose a robust technique
for automatic detection of COVID-19 pneumonia from digital chest X-ray images
applying pre-trained deep-learning algorithms while maximizing the detection
accuracy. A public database was created by the authors combining several public
databases and also by collecting images from recently published articles. The
database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579
normal chest X-ray images. Transfer learning technique was used with the help
of image augmentation to train and validate several pre-trained deep
Convolutional Neural Networks (CNNs). The networks were trained to classify two
different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and
COVID-19 pneumonia with and without image augmentation. The classification
accuracy, precision, sensitivity, and specificity for both the schemes were
99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%,
respectively.Comment: 12 pages, 9 Figure
Respiratory Sound Analysis for the Evidence of Lung Health
Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinicianâs lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds
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