Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

Comparative Study of VGG-13, AlexNet, MobileNet and Modified-DarkCovidNet for Chest X-Ray Classification

Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.

COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3-layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images;edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that has no correlation with each other. The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset, and the findings that we have obtained are quite encouraging. © 2023 Tech Science Press. All rights reserved.

A New Convolution Neural Layer Based on Weights Constraints

With the rapid development of artificial intelligence techniques, emerging deep neural networks (DNN) is one of the most effective ways to solve many challenges. Convolution neural networks (CNNs) are considered one of the most popular AI techniques used to extract and analyze meaningful features for image datasets, especially in the medical diagnosis field. In this paper, a proposed constrained convolution layer (COCL) for the CNN model is proposed. The new layer uses a constrained number of weights in each kernel trained in the phase of learning and excludes the others weights with zero values. The proposed method is introduced to extract a special type of feature considering the local shape of a sub-image (window) and topological relations between group pixels. The features extract according to a random distribution of weights in kernels that are determined considering a particular desired percentage. Furthermore, this paper proposed a CNN model architecture that uses COCL rather than the traditional CNN layer (TCL). The efficiency of the method is evaluated using three types of medical image datasets compared with the traditional convolution layer, pre-trained deep neural networks (pre-DNNs), and state-of-art methods. The proposed model outperforms other methods in terms of accuracy and F1 score metrics and exceeds more than 98%, 89%, and 93% for the three datasets used in the evaluation, respectively. © 2022 IEEE.

SCN-SAM: A Modified Self-Cure Network for Facial Expression Recognition under Face Masks

Due to COVID-19, wearing masks has become more common. However, it is challenging to recognize expressions in the images of people wearing masks. In general facial recognition problems, blurred images and incorrect annotations of images in large-scale image datasets can make the model's training difficult, which can lead to degraded recognition performance. To address this problem, the Self-Cure Network (SCN) effectively suppresses the over-fitting of the network to images with uncertain labeling in large-scale facial expression datasets. However, it is not clear how well the SCN suppresses the uncertainty of facial expression images with masks. This paper verifies the recognition ability of SCN on images of people wearing masks and proposes a self-adjustment module to further improve SCN (called SCN-SAM). First, we experimentally demonstrate the effectiveness of SCN on the masked facial expression dataset. We then add a self-adjustment module without extensive modifications to SCN and demonstrate that SCN-SAM outperforms state-of-the-art methods in synthetic noise-added FER datasets. © 2023 Global IT Research Institute (GiRI).

Explainable COVID-19 Three Classes Severity Classification Using Chest X-Ray Images

COVID-19 has been raging for almost three years ever since its first outbreak. It is without a doubt that it is a common human goal to end the pandemic and how it was before it started. Many efforts have been made to work toward this goal. In computer vision, works have been done to aid medical professionals into faster and more effective procedures when dealing with the disease. For example, disease diagnosis and severity prediction using chest imaging. At the same time, vision transformer is introduced and quickly stormed its way into one of the best deep learning models ever developed due to its ability to achieve good performance while being resources friendly. In this study, we investigated the performance of ViT on COVID19 severity classification using an open-source CXR images dataset. We applied different augmentation and transformation techniques to the dataset to see ViT's ability to learn the features of the different severity levels of the disease. It is concluded that training ViT using the horizontally flipped images added to the original dataset gives the best overall accuracy of 0.862. To achieve explainability, we have also applied Grad-CAM to the best performing model to make sure it is looking at relevant region of the CXR image upon predicting the class label. © 2022 IEEE.

A New Hybrid Artificial Intelligence Model for Diseases Identification

Artificial intelligence is increasingly applied in many fields, specially in medicine to assist patients and physicians. Growing datasets provide a sound basis to adapt machine learning methods to identify and detect some diseases. These later, are often very similar which make difficult their identification by chest X-ray images. In this paper, we introduce a diagnostic AI model that allow to separate, diagnose and classify three various diseases: tuberculosis, covid19 and Pneumonia. The proposed model is based on a combination of Deep Learning using the deep SqueezeNet model and Machine Learning: SVM, KNN, Logistic Regression, decision tree and Naive Bayes. The model is applied to a chest X-ray dataset containing images for each type of disease. To train and test our model, we split the image dataset into two training and test subsets in order to differentiate between different disease types. The accuracy show clearly that our model provides better results of diagnosis and identification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets

Over the last few years, Deep Learning models have shown prominent results in medical image analysis especially to predict disease at the earlier stages. Since Deep Neural Network require more training data for better prediction, it needs more computational time for training. Transfer learning is a technique which uses the learned knowledge to perform the classification task by minimizing the number of training data and training time. To increase the accuracy of a single classifier, ensemble learning is used as a meta-learner. This research work implements a framework Ensemble Pre-Trained Deep Convolutional Neural Network using Resnet50, InceptionV3 and VGG19 pre-trained Convolutional Neural Network models with modified top layers to classify the disease present in the medical image datasets such as Covid X-Rays, Covid CT scans and Brain MRI with less computational time. Further, these models are combined using stacking and bagging ensemble approach to increase the accuracy of single classifier. The datasets are distributed as train, test and validation data and the models are trained and tested for four epochs. All the models are evaluated using validation data and the result shows that the ensemble learning approach increases the prediction accuracy when compared to the single models for all the datasets. In addition, this experiment reveals that the stacked model attains higher test accuracy of 99% for chest X-Ray images, 100% for chest CT scan images and 98% for brain MRI, compared to the bagged models. © 2022 IEEE.

IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR COVID19 DETECTION USING X-RAY AND VOICE DATASET

Ever since the deadly corona virus came into existence the life of the people has been shattered both in terms of health and economic crisis. Even today its various variants are creating havoc among the people. The traditional way of testing the disease is time consuming and is also not cost efficient due to the requirement of PEP kits. In this paper various Machine Learning (ML) techniques have been implemented based on the cough samples and chest x-ray images of the individuals. A hybrid model with GUI interface is designed to predict covid-19 and to perform the comparative analysis of sequential model and ResNet50 for image dataset, CNN with hyper parameter tuning and CNN for voice dataset. From the experimental analysis, ResNet50 performed better when compared to sequential model for image dataset and CNN with hyper parameter tuning performed better when compared with CNN model for voice dataset. © 2022 IEEE.

AM-DenseNet:A Novel DenseNet Framework using Attention Mechanisms for COVID-19 CT Image Classification

We present a novel DenseNet framework with attention mechanisms (AM-DenseNet) to extract lung feature of 1 COVID-19 patient. In AM-DenseNet, a lightweight Efficient Channel Attention (ECA) structure is added at the end of each dense connection to introduce an attention mechanism to discovery local lesion domain. We compare our AM-DenseNet to VGG-16, ResNet-50 and DenseNet-121 on CT image dataset of COVID-19 patients. According to the experimental results, we conclude that the classification performance of AM-DensNet framework can be significantly enhanced under the effect of attention mechanism. The AM-DensNet shows better classification performance than the compared models. © 2022 IEEE.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so early and simple detection of tuberculosis has been more and more important. In our study,we focused on tuberculosis diagnosis by using the chestX-ray image, the essential input for the radiologist's profession, and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images. We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types. We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models. Our experiments were carried out by applying three different architectures, Alexnet, Resnet, and Densenet, on international, Vietnamese, and combined X-ray image datasets. After training, all models were verified on a pure Vietnamese X-rays set. The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC (Area under the Receiver Operating Characteristic Curve), sensitivity, specificity, and accuracy. In the best strategy, most of the scores were more than 0.93, and all AUCs were more than 0.98. © 2023 Tech Science Press. All rights reserved.

COVID-19 Detection Through Deep Learning Algorithms using Chest X-ray Images

COVID-19 became a pandemic affecting the lives of every human globally by the end of 2019. The disease impaired the lungs of infected patients. Precise prediction and diagnosis of COVID-19 disease are challenging due to its resemblance to viral pneumonia. Using multiple deep learning approaches, the researchers used chest X-ray (CXR) imaging to diagnose COVID-19. The X-ray image dataset from Kaggle is used for the study by selecting the COVID-19 and normal class. InceptionV3, MobileNetV2, VGG19,VGG16 and ResNet50 are the five neural networks used for binary classification of COVID-19. The accuracy of MobileNetV2 surpasses that of the remainder of the model by 93.02%. However, it has a compilation time of 1836 seconds per epoch. Besides, VGG16 has an accuracy of 92.37%, with a compilation time of 603 seconds per epoch. Compared to these models, Inceptonv3, Resnet50 and VGG19 perform with an accuracy score of 86.42%, 68.34% and 91.79%. Applying deep learning techniques to COVID-19 radiological imaging holds great promise for enhancing the accuracy of diagnosis when in comparison to the gold standard RT-PCR test and assisting healthcare professionals in making decisions quickly © 2022 IEEE.

Detecting COVID-19 in Chest X-Ray Images Using Apache Spark and CNN

COVID-19 is highly contagious and highly pathogenic, It seriously threatens human life and health. Rapid detection of positive COVID-19 cases is very important in stopping the spread of the virus. At early diagnosis, It is the most simple and rapid indicator for judging changes in the illness. As the COVID-19 chest X-ray image dataset continues to expand, Researchers build a CNN-based COVID-19 detection model on Apache Spark. The model can effectively detect positive cases of COVID-19. This article first introduces the big data platform Apache Spark, Deep Learning Technology CNN, transfer learning techniques, etc. Then, it summarizes the characteristics and deficiencies of the research on chest X-ray image recognition of COVID-19 in recent years. Finally, Under the big data thinking, This paper proposes a technical direction for rapid detection of COVID-19 based on the big data analysis platform Apache Spark and the deep learning algorithm CNN for large-scale COVID-19 chest X-ray image datasets. © 2022 WCSE. All Rights Reserved.

Explainable Augmented Intelligence and Deep Transfer Learning for Pediatric Pulmonary Health Evaluation

Biomedical Instrumentation is one of the fastest health emerging innovative technologies with proven contribution towards interdisciplinary medicine, it helps physicians to diagnose complex medical problems and provide treatment to patients precisely and safely. With this technological trend, explainable artificial intelligence, biomedical image processing and augmented intelligence can provide a tool that can help pediatricians, pulmonology and otolaryngology physicians, epidemiologists and pediatric practitioners to interpretably and reliably diagnose chronic and acute respiratory disorders in children, adolescents and infants. Unfortunately, the reliability of digital image processing for pulmonary disease diagnosis often depends on availability of large chest X-ray image datasets. This work presents a reliable interpretable deep transfer learning approach for pediatric pulmonary health evaluation regardless of the scarcity and limited annotated pediatric chest X-ray Image dataset sizes. This approach leverages a combination of computer vision tools and techniques to reduce child morbidity and mortality through predictive and preventive medicine with reduced surveillance risks and affordability in low resource settings. With open datasets, the deep neural networks classified the generated augmented images into 4 classes namely;Normal, Covid-19, Tuberculosis and Pneumonia at an accuracy of 97%, 97%, 70%, and 73% respectively with recall of 100% for Pneumonia and overall accuracy of 79% at only 10 epochs for both regular and transferred learning. © 2022 IEEE.

Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays

Since the beginning of the COVID-19 pandemic, more than 350 million cases and 5 million deaths have occurred. Since day one, multiple methods have been provided to diagnose patients who have been infected. Alongside the gold standard of laboratory analyses, deep learning algorithms on chest X-rays (CXR) have been developed to support the COVID-19 diagnosis. The literature reports that convolutional neural networks (CNNs) have obtained excellent results on image datasets when the tests are performed in cross-validation, but such models fail to generalize to unseen data. To overcome this limitation, we exploit the strength of multiple CNNs by building an ensemble of classifiers via an optimized late fusion approach. To demonstrate the system’s robustness, we present different experiments on open source CXR datasets to simulate a real-world scenario, where scans of patients affected by various lung pathologies and coming from external datasets are tested. Promising performances are obtained both in cross-validation and in external validation, obtaining an average accuracy of 93.02% and 91.02%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detection of Covid-19 From an Imbalanced Chest X-ray Image Data Set

The Covid-19 pandemic has spread quickly, making identification of the virus critically important in assisting overburdened healthcare systems. Numerous techniques have been used to identify Covid-19, of which the Polymerase chain reaction (PCR) test is the most common. However, obtaining results from the PCR test can take up to two days. An alternative is to use X-ray images of the subject's chest area as inputs to a deep learning neural networks algorithm. The two problems with this approach are the choice of architecture and the method used to deal with the imbalanced data. In this study a comparative analysis of a standard convolutional neural network (CNN) and a number of transfer learning algorithms with a range of imbalanced data techniques was conducted to detect Covid-19 from a data set of chest x-ray images. This data set was an amalgamation of two data sets extracted from the Kaggle Covid-19 open source data repository and non-Covid illnesses taken from the National Institute of Health. The resulting data set was had over 115k records and 15 different type of findings ranging from no-illness to illnesses such as Covid-19, emphysema and lung cancer. This study addresses the problem of class imbalance on the largest data set used for x-ray detection of Covid-19 by combining undersampling and oversampling methods. The results showed that a CNN model in conjunction with these random over and under sampling methods outperformed all other candidates when identifying Covid-19 with a F1-score of 93%, a precision of 90% and a recall of 91%. © 2021 CEUR-WS. All rights reserved.

COVID-19 PREDICTION THROUGH CHEST X-RAY IMAGE DATASETS USING DEEP LEARNING

The work aims at the prediction and analysis of COVID-19 from Chest X-Ray scan images using Pre-trained Deep Convolutional Neural Network models. Analysis is carried out using two open-source datasets, to identify and differentiate between the Chest X-Ray scans of non COVID person and COVID-19 affected person. A baseline model using LeNet-5 is implemented using the initial dataset collected, which gave 98.57 % accuracy. Further, pre-trained models such as AlexNet, ResNet 50, Inception V3, VGG16, VGG19 and Xception are used for COVID prediction and carryout comparative analysis. Using the performance measures viz. Accuracy, Confusion Matrix and ROC Curves, the result of study shows that for the first dataset used for analysis, Xception and for the second dataset Inception architectures respectively are most suitable for the prediction of COVID-19. © 2021 IEEE.

COVID-19 classification based on CNNs models in CT image datasets

The COVID-19 coronavirus pandemic was a global challenge to the whole society and at the same time created a unique situation for the development of science, scientific communication and open access to scientific information. At the beginning of 2019 the world has faced a pandemic of Covid-19 coronavirus. The coronavirus impacted dramatically lives of majority people around the globe. Deep learning methods allow automatic classification of the coronavirus disease from the computer tomography (CT) scans of the lung. In our work we test several popular convolutional neural network (CNN) models to classify slices of the CT scans. In this study we indicate that the VGG-19 model gives the best classification accuracy among the other tested models such as DenseNet201, MobileNetV2, Xception, VGG-16 and ResNet50v2. In particular, the model achieves the accuracy of 99.08% for CovidX CT Dataset and 98.44% for SARS-CoV-2 CT dataset and 92.30% for UCSD COVID-CT dataset. Additionally, our results include 3D heatmaps that explain classification results for each individual model, showing regions of the lung affected by the coronavirus. © 2021 IEEE.

Localized Feature Aggregation Module for Semantic Segmentation

We propose a new information aggregation method which called "Localized Feature Aggregation Module"based on the similarity between the feature maps of an encoder and a decoder. The proposed method recovers positional information by emphasizing the similarity between decoder's feature maps with superior semantic information and encoder's feature maps with superior positional information. The proposed method can learn positional information more efficiently than conventional con-catenation in the U-net and attention U-net. Additionally, the proposed method also uses localized attention range to reduce the computational cost. Two innovations contributed to improve the segmentation accuracy with lower computational cost. By experiments on the Drosophila cell image dataset and COVID-19 image dataset, we confirmed that our method outperformed conventional methods. © 2021 IEEE.

COVID-19 detection with X-Ray input data

This paper presents a system for COVID-19 detection with chest X-Ray scans as input data. The detection engine is implemented with Deep neural networks. The model of the generated Deep Learning Neural Network is trained with the use of chest X-Ray scans dataset as input data. The trained model was tested with new test image datasets and the results show that it provides a high enough recognition rate, providing that this methodology can be applied for quick and nonintrusive COVID-19 detection. © 2021 IEEE.