Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

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On Teacher-Student Semi-Supervised Learning for Chest X-ray Image Classification

10.21528/cbic2021-80 ◽

2021 ◽

Author(s):

Roberto Augusto Philippi Martins ◽

Danilo Silva

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Algorithm ◽

Unlabeled Data ◽

Imaging Data ◽

X Ray ◽

Teacher Student ◽

Chest X Ray ◽

Deep Learning Model

The lack of labeled data is one of the main prohibiting issues on the development of deep learning models, as they rely on large labeled datasets in order to achieve high accuracy in complex tasks. Our objective is to evaluate the performance gain of having additional unlabeled data in the training of a deep learning model when working with medical imaging data. We present a semi-supervised learning algorithm that utilizes a teacher-student paradigm in order to leverage unlabeled data in the classification of chest X-ray images. Using our algorithm on the ChestX-ray14 dataset, we manage to achieve a substantial increase in performance when using small labeled datasets. With our method, a model achieves an AUROC of 0.822 with only 2% labeled data and 0.865 with 5% labeled data, while a fully supervised method achieves an AUROC of 0.807 with 5% labeled data and only 0.845 with 10%.

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COVID-19 detection in Chest X-ray Images using Deep Learning

10.21203/rs.3.rs-65954/v1 ◽

2020 ◽

Author(s):

Ebru Erdem ◽

Tolga Aydın

Keyword(s):

Deep Learning ◽

Deep Neural Networks ◽

Specific Information ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Lung Infections ◽

Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

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Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning from Chest X-Ray Images of Patients

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v8i2.6517 ◽

2021 ◽

Author(s):

Amir Sorayaie Azar ◽

Ali Ghafari ◽

Mohammad Ostadi Najar ◽

Samin Babaei Rikan ◽

Reza Ghafari ◽

...

Keyword(s):

Deep Learning ◽

Bacterial Pneumonia ◽

Viral Pneumonia ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Detection And Diagnosis ◽

Fold Cross Validation

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases. Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method. Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively. Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19.

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A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning

Journal of Healthcare Engineering ◽

10.1155/2021/1002799 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Omar Faruk ◽

Eshan Ahmed ◽

Sakil Ahmed ◽

Anika Tabassum ◽

Tahia Tazin ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Computer Assisted ◽

Promising Technique ◽

Suggested Approach ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v1 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v2 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

Electronics ◽

10.3390/electronics10161996 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1996

Author(s):

Junghoon Park ◽

Il-Youp Kwak ◽

Changwon Lim

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Model ◽

Fine Tuning ◽

Classification Model ◽

World Health ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Deep Learning Model

The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

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Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Applied Sciences ◽

10.3390/app9224871 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4871 ◽

Cited By ~ 4

Author(s):

Quan Liu ◽

Chen Feng ◽

Zida Song ◽

Joseph Louis ◽

Jian Zhou

Keyword(s):

Deep Learning ◽

Model Comparison ◽

Surveillance Systems ◽

Comparison Study ◽

Learning Models ◽

The Core ◽

Dump Trucks ◽

Deep Learning Model ◽

Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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