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Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from chest x-rays Images

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Computers, Materials & Continua 2024年第7期80卷 1055-1073页

作者： Anas AbuKaraki Tawfi Alrawashdeh Sumaya Abusaleh Malek Zakarya Alksasbeh Bilal Alqudah Khalid Alemerien Hamzah Alshamaseen Faculty of Information Technology Al-Hussein Bin Talal UniversityMa’an71111Jordan College of Information and Communications Technology(ICT) Tafila Technical UniversityTafila66110Jordan IOTISTIC Solutions Company Amman11185Jordan

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the chestx-ray14,Padchest,and Chexpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,***,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the x-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation *** classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW *** proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for *** experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient ***,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on x-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

关键词： Image classification decision support system EfficientNet-V1-B4 AdamW optimizer pulmonary edema pleural effusion chest x-rays

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COVID-19 Automatic Detection Using Deep Learning

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Computer Systems Science & Engineering 2021年第10期39卷 15-35页

作者： Yousef Sanajalwe Mohammed Anbar Salam Al-E’mari National Advanced IPv6 Centre of Excellence(Nav6) Universiti Sains Malaysia11800 USMPenangMalaysia Computer Science Department Northern Border University(NBU)9280 NBUAr’arthe Kingdom of Saudi Arabia

The novel coronavirus disease 2019(COVID-19)is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of *** first step to mitigate and control its spread is to identify and isolate the infected ***,because of the lack of reverse transcription polymerase chain reaction(RT-CPR)tests,it is important to discover suspected COVID-19 cases as early as possible,such as by scan analysis and chest x-ray by ***,chest x-ray analysis is relatively time-consuming since it requires more than 15 minutes per *** this paper,an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 *** proposed model consists of three main stages:image segmentation using Harris Hawks optimizer,synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network,and image classification using Conventional Neural *** chest x-ray images datasets are used to train and test the proposed *** demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive *** achieved 99.4%accuracy,99.15%precision,99.35%recall,99.25%F-measure,and 98.5%specificity.

关键词： Conventional neural network COVID-19 deep learning enhanced Wasserstein and auxiliary classifier generative adversarial network image classification image segmentation chest x-rays

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Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

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Virtual Reality & Intelligent Hardware 2022年第4期4卷 292-305页

作者： Imran AHMED Misbah AHMAD Gwanggil JEON School of Computing and Information Science Faculty of Science and EngineeringAnglia Ruskin UniversityEast RoadCambridgeEnglandUK Center of Excellence in IT Institute of Management SciencesHayatabadPeshawarPakistan Department of Embedded Systems Engineering Incheon National UniversityIncheonKorea

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and *** twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of *** the present COVID-19 pandemic,various medical devices,such as x-rays and CT scan machines and processes,are constantly being used to collect and analyze medical *** collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and *** To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/***,medical images,that is,x-rays,are analyzed by using a deep-learning model to detect the infection of *** designed system is based on the cascade recurrent convolution neural network(RCNN)*** this architecture,the detector stages are deeper and more sequentially selective against small and close false *** architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the *** each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different *** this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of *** train the model by using x-ray images as the model was previously trained on another *** The developed system achieves good accuracy during the detection phase of *** experimental o

关键词： Digital twins Deep learning Healthcare COVID-19 chest x-rays Artificial intelligence

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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the x-radiation(x-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest x-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest x-rays *** dataset used in this study contains 1504 chest x-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest x-rays machine learning

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Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

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Computer Systems Science & Engineering 2023年第8期46卷 1337-1353页

作者： José Escorcia-Gutierrez Roosvel Soto-Diaz Natasha Madera Carlos Soto Francisco Burgos-Florez Alexander Rodríguez Romany F.Mansour Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Mechatronics Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Medicine Universidad del NorteBarranquilla081007Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest x-ray(CxR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CxR for TB screening and *** the same time,CxR interpretation is a time-consuming and subjective ***,high resemblance among the radiological patterns of TB and other lung diseases can result in ***,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB *** this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on chest x-rays(CxR).The presented WSODTL-TBC model aims to detect and classify TB on CxR ***,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image ***,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature *** addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the *** performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many *** experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

关键词： Computer-aided diagnosis water strider optimization deep learning chest x-rays transfer learning

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The Role of chest x-Ray in Monitoring Lung Changes among COVID-19 Patients in Gaza Strip

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Open Journal of Medical Imaging 2021年第2期11卷 29-47页

作者： Mahmoud Mousa Marwan Matar Yasser Al Ajerami Ahmad Naijm Khalid Abu Shab Sadi Jaber Fouad SJaber Hazem Dawoud Department of Radiology Gaza-European Hospital Gaza Strip Palestine Department of Medical Imaging Applied Medical Sciences Al-Azhar University Gaza Strip Palestine Department of Nursing Applied Medical Sciences Al-Azhar University Gaza Strip Palestine Department of Radiology Nasser Medical Complex Gaza Strip Palestine Valley Fever Institute UCLA Kern Medical Bakersfield USA Department of Chemistry and Biochemistry Ohio University Athens USA

Objective: To investigate the time course and findings severity of COVID-19 infection at chest radiography based on a 6-point radiological severity score, and correlates these with patients’ age and gender. Methods: This is a retrospective study of COVID-19 patients who were admitted at European Gaza Hospital and evaluated between October 6, 2020, and November 30, 2020. Baseline and serial chest radiographs, up to 4 images per patient, were reviewed and assessed for predominant pattern, side, and location of lung opacity. Utilized a 6-point scoring system, which divides the chest x-ray into 6 zones, to assess chest x-ray changes and correlate them with the severity of infection, age, and gender of patients. Results: The study included 136 COVID-19 patients: (51/136, 37%) were males and (85/136, 62.5%) were females, while age ranged from 7 months to 90 years with a mean age of 41.7 ± (19.5) years. Negative chest x-rays were more observed than positive images. Ground-glass opacity was the most frequent pattern with a decreasing trend from 1st to 4th chest x-ray (from 33.8% to 3.7%), followed by consolidation (from 16.2% to 2.9%). Also, the commonest pattern of opacity was seen in peripheral areas (27/136, 19.9%), lower zone location (23/136, 16.9%), and bilateral opacity involvement (43/136;31.6%). No significant correlation was noticed between the patient’s gender, age, and severity score (P > 0.05). Conclusions: The 6-point chest x-ray severity score as a predictive tool in assessing the severity due to provide an assessment of the progression or regression pathway.

关键词： chest x-rays COVID 19 Lung Changes Scoring System Gaza Strip

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