COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm

AbstractComputer-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-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

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CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

10.1101/2021.05.19.21257430 ◽

2021 ◽

Author(s):

Md. Saikat Islam Khan ◽

Anichur Rahman ◽

Md. Razaul Karim ◽

Nasima Islam Bithi ◽

Shahab Band ◽

...

Keyword(s):

Healthcare Professionals ◽

Feature Fusion ◽

State Of The Art ◽

Research Community ◽

Diagnostic Model ◽

X Ray ◽

Multi Scale ◽

The World ◽

Chest X Ray ◽

Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

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A Transfer Learning Method for Pneumonia Classification and Visualization

Applied Sciences ◽

10.3390/app10082908 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2908 ◽

Cited By ~ 5

Author(s):

Juan Luján-García ◽

Cornelio Yáñez-Márquez ◽

Yenny Villuendas-Rey ◽

Oscar Camacho-Nieto

Keyword(s):

Transfer Learning ◽

Causes Of Death ◽

State Of The Art ◽

Machine Learning Algorithms ◽

Learning Method ◽

X Ray ◽

Chest X Ray ◽

Aided Diagnosis ◽

Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

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

10.21203/rs.3.rs-65954/v2 ◽

2020 ◽

Author(s):

Ebru Erdem ◽

Tolga Aydın

Keyword(s):

Deep Learning ◽

Deep Neural Networks ◽

Empirical Evaluation ◽

Specific Information ◽

The Novel ◽

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 the analyses were performed on a publicly available CXR dataset. This study 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 a disease. Empirical evaluation demonstrates 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. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.

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A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images

10.21203/rs.3.rs-963338/v1 ◽

2021 ◽

Author(s):

Shaw-Hwa Lo ◽

Yiqiao Yin

Keyword(s):

State Of The Art ◽

Image Data ◽

Prediction Performance ◽

Data Set ◽

X Ray ◽

Real World Application ◽

Explainable Ai ◽

Chest X Ray ◽

High Prediction ◽

Visualization Techniques

Abstract In the field of eXplainable AI (XAI), robust “black-box” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, ex-plainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology – Influence Score (I-score) – to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictiv-ity. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and in-terpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.

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AI-based Diagnosis of COVID-19 Patients Using X-ray Scans with Stochastic Ensemble of CNNs

10.36227/techrxiv.12464402.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ridhi Arora ◽

Vipul Bansal ◽

Himanshu Buckchash ◽

Rahul Kumar ◽

Vinodh J Sahayasheela ◽

...

Keyword(s):

Comprehensive Evaluation ◽

State Of The Art ◽

Empirical Evaluation ◽

Main Idea ◽

Feature Space ◽

Disease Diagnosis ◽

X Ray ◽

Main Challenge ◽

Latent Space ◽

Chest X Ray

<div>According to WHO, COVID-19 is an infectious disease and has a significant social and economic impact. The main challenge in ?fighting against this disease is its scale. Due to the imminent outbreak, the medical facilities are over exhausted and unable to accommodate the piling cases. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep representations over a gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides</div><div>a fast diagnosis of COVID-19 and can scale seamlessly. This work presents a comprehensive evaluation of previously proposed approaches for X-ray based</div><div>disease diagnosis. Our approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets,</div><div>and then linearly regressing the predictions from an ensemble of classifi?ers which take the latent vector as input. We experimented with publicly available datasets having three classes { COVID-19, normal, Pneumonia. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset with fi?ve different very similar diseases. Extensive empirical evaluation shows</div><div>how the proposed approach advances the state-of-the-art.</div>

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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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A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Applied Sciences ◽

10.3390/app11199023 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9023

Author(s):

Najam-ur Rehman ◽

Muhammad Sultan Zia ◽

Talha Meraj ◽

Hafiz Tayyab Rauf ◽

Robertas Damaševičius ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

State Of The Art ◽

Detection System ◽

Classification Methods ◽

Single Class ◽

X Ray ◽

Computer Aided ◽

Chest X Ray ◽

Fully Connected

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i3.pp1469-1480 ◽

2021 ◽

Vol 24 (3) ◽

pp. 1469

Author(s):

Oussama Dahmane ◽

Mustapha Khelifi ◽

Mohammed Beladgham ◽

Ibrahim Kadri

Keyword(s):

Transfer Learning ◽

Medical Center ◽

Histogram Equalization ◽

Data Set ◽

X Ray ◽

Training Techniques ◽

Proposed Model ◽

Improve Accuracy ◽

Learning Technique ◽

Chest X Ray

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).

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AI-based Diagnosis of COVID-19 Patients Using X-ray Scans with Stochastic Ensemble of CNNs

10.36227/techrxiv.12464402 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ridhi Arora ◽

Vipul Bansal ◽

Himanshu Buckchash ◽

Rahul Kumar ◽

Vinodh J Sahayasheela ◽

...

Keyword(s):

Comprehensive Evaluation ◽

State Of The Art ◽

Empirical Evaluation ◽

Main Idea ◽

Feature Space ◽

Disease Diagnosis ◽

X Ray ◽

Main Challenge ◽

Latent Space ◽

Chest X Ray

<div>According to WHO, COVID-19 is an infectious disease and has a significant social and economic impact. The main challenge in ?fighting against this disease is its scale. Due to the imminent outbreak, the medical facilities are over exhausted and unable to accommodate the piling cases. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep representations over a gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides</div><div>a fast diagnosis of COVID-19 and can scale seamlessly. This work presents a comprehensive evaluation of previously proposed approaches for X-ray based</div><div>disease diagnosis. Our approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets,</div><div>and then linearly regressing the predictions from an ensemble of classifi?ers which take the latent vector as input. We experimented with publicly available datasets having three classes { COVID-19, normal, Pneumonia. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset with fi?ve different very similar diseases. Extensive empirical evaluation shows</div><div>how the proposed approach advances the state-of-the-art.</div>

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