rashid mazhar

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R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification

Cornell University - arXiv, Sep 29, 2022

Restoration of poor quality images with a blended set of artifacts plays a vital role for a relia... more Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such as image deblurring, denoising, and exposure correction where there is usually a strong assumption on the artifact type and severity. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model keeps any disease intact after the restoration. Therefore, this will naturally lead to a higher diagnosis performance thanks to the improved X-ray image quality. To accomplish this crucial objective, we define the restoration task as an Image-to-Image translation problem from poor quality having noisy, blurry, or over/under-exposed images to high quality image domain. The proposed R2C-GAN model is able to learn forward and inverse transforms between the two domains using unpaired training samples. Simultaneously, the joint classification preserves the disease label during restoration. Moreover, the R2C-GANs are equipped with operational layers/neurons reducing the network depth and further boosting both restoration and classification performances. The proposed joint model is extensively evaluated over the QaTa-COV19 dataset for Coronavirus Disease 2019 (COVID-19) classification. The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model. Moreover, in the qualitative analysis, the restoration performance of R2C-GANs is approved by a group of medical doctors. We share the software implementation at https://github.com/meteahishali/R2C-GAN.

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Fully automated 2D and 3D convolutional neural networks pipeline for video segmentation and myocardial infarction detection in echocardiography

Multimedia Tools and Applications

Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data includi... more Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especially when patients are facing MI and their lives are at stake. In this paper, we propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN) to detect MI depending on regional wall motion abnormalities (RWMA) of the left ventricle (LV) from videos produced by echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI. We trained both CNNs on a dataset composed of 165 echocardiogra

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P063 A mechatronic bio-mimicking simulator to study various combinations of mechanical compressions during CPR

Resuscitation

A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images

ArXiv, 2020

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M. Benammar, R. Mazhar, M. F. Al-Sa’d, M. Abu-Gaoud, E. A. Alizadeh, “Design of a Digital Pleural Drainage System,” Proc. The 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2012), Volume 3, pp. 157-160, 17-20 May 2012, Shanghai, China

Vascular injury during Needle thoracentesis: need for a change in landmarks. A case report and review of the literature. JEMTAC June 2008;8:128 (ISSN: 19997086)

Journal of Emergency Medicine, Trauma and Acute Care

Extreme cardiac and pulmonary artery compression causing positional oxygen desaturation

Emergency Medicine Journal, 2008

A 30-year-old man presented to accident and emergency with a 6-month history of worsening dyspnoe... more

Blind ECG Restoration by Operational Cycle-GANs

Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early det... more Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attempted ECG denoising, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the...

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Early Myocardial Infarction Detection over Multi-view Echocardiography

ArXiv, 2021

Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a bl... more Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in. Echocardiography is the fundamental tool to assess any RWMA. Assessing the motion of the left ventricle (LV) wall only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2chamber (A2C) views in which a total of 11 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the LV wall by Active Polynomials (APs), which extract and track the ...

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Dynamic Ventilatory circuit divider": Safer way to multiply the number of available ventilators

Background: Recent pandemic has brought a sudden surge in the requirement of mechanical ventilato... more Background: Recent pandemic has brought a sudden surge in the requirement of mechanical ventilators all over the world. In this backdrop, there is wide interest in looking for ways to support multiple patients from a single ventilator. Various solutions, based upon simple mechanical division of the ventilator tubings are described. However, recently warnings have been issued by multiple international professional societies against these plumbing solutions as it can seriously harm the patients. Methods: We have bifurcated the inspiratory and expiratory conduits from a single ventilator with addition of one way valves, pressure and flow sensors along with volume and PEEP control. A purpose-built software and a low-cost microcontroller based control system integrates and displays the data in the familiar ventilatory graphic and numerical format onto a generic screen. The system is calibrated with simulated lungs with varying compliance. In addition to the standard microbial, heat and moisture exchanger filters we design to add UV-C lights at 254-260 nanometre wavelength in the expiratory channel for its virucidal effect. Results: The "dynamic ventilatory divider" system is capable of providing and controlling individual flow, tidal volume (TV) and Positive End-expiratory pressure (PEEP) for individual patients. Furthermore, it would also display the ventilatory parameters of both the patients, on a single split screen, in a familiar format. FiO2 and rate are still controlled by the mother ventilator. Conclusions: The prototype system has a potential to provide safe ventilation to at least two individuals from a single ventilator, while maintaining the unique requirements of each patient.

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A series of unfortunate events: Mind the windpipe

Clinical Case Reports, 2020

Tracheal stenosis can be a disastrous consequence of prolonged endotracheal intubation. In the CO... more

Early Detection of Myocardial Infarction in Low-Quality Echocardiography

IEEE Access, 2021

Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health probl... more Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall using a state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are highly accurate segmentation of the LV wall from low-quality echocardiography, pseudo labeling approach for ground-truth formation of the unannotated LV wall, and the first public echocardiographic dataset (HMC-QU) a for MI detection. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed approach has achieved 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection on the HMC-QU dataset.

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Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images

IEEE Access, 2021

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first kn... more Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity. INDEX TERMS COVID-19 detection in early stages, deep learning, machine learning, representation based classification.

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Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion a... more Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their “maximum motion displacement” plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database ...

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Taxonomy of Communications in the Operating Room

Advances in Intelligent Systems and Computing, 2017

US hospitals are facing critical problems of nurse shortage. One report predicts that there will ... more US hospitals are facing critical problems of nurse shortage. One report predicts that there will be a shortage of 260,000 registered nurses by 2025 in the USA, and it was found out that patient mortality risk is 6% higher in hospitals understaffed with nurses compared to units fully staffed. One possible solution to cope with such nurse shortage problem is to develop robotic scrub nurses that can collaborate with surgeons. Some robotic systems have been specifically developed to handle instruments to the surgeon in the operating room. These robotic systems work under the assumption that verbal and gesture communication are the most common modalities. This study shows that expert surgical staff do not use specific gestures to communicate and rely only in two modalities: predictions performed by the assistant and verbal commands. From a group of 68 instruments delivered, during three cardiothoracic surgeries, 47 corresponded to successful predictions and 23 to verbal commands. Only one wrong prediction was observed but no specific gesture.

Machine learning screening of COVID-19 patients based on X-ray images for unbalanced classes

Journal of Emergency Medicine, Trauma and Acute Care, 2021

Background: COVID-19 is a pandemic that had already infected more than forty-six million people a... more Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, a...

Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

2021 9th European Workshop on Visual Information Processing (EUVIP), 2021

COVID-19 is a virus that has infected more than one hundred and fifty million people and caused m... more COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with wellknown classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection.

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COVID-19 infection map generation and detection from chest X-ray images

Health Information Science and Systems, 2021

Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 20... more Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-calledinfection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annot...

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Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization

IEEE Access, 2020

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of ... more Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image preprocessing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

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Can AI Help in Screening Viral and COVID-19 Pneumonia?

IEEE Access, 2020

) is a pandemic disease, which has already caused thousands of causalities and infected several m... more ) 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 the 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. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources. INDEX TERMS Artificial intelligence, COVID-19 pneumonia, machine learning, transfer learning, viral pneumonia, computer-aided diagnostic tool.

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