COVID-19 Detection on X-Ray Image Using Deep Learning

COVID-19 is one of the threats that came out of nowhere and literally shook the entire world. Various prediction techniques have been invented in a very short time. This study also develops a Deep Learning (DL) model which can predict the presence of COVID-19 and pneumonia by analyzing the X-ray images of human lungs. From Kaggle, a collection of X-ray images of the lungs is collected. Then, this dataset is preprocessed using two alternative methods. Some of the techniques include image enhancement and picture resizing. The two deep-learning models are then trained using the preprocessed dataset. A few more examples of DL algorithms include MobileNet and Inception-V3. The best model is then selected by validating the learned deep-learning models. As the epochs count increases during training and validation, the accuracy value for both models increases. The value of the loss increases as the number of epochs decreases. During the fourteenth validation period, the model generates a loss value of 0.32 for the MobileNet technique. During the first few training epochs, accuracy is lower, and by the fifteenth, it is close to 0.9. The Inception-V3 method produces a loss value of 0.1452 at the eleventh validation epoch, which is the lowest value. The greatest accuracy value of 0.9697 is obtained after the twelfth cycle of validation. The model that performs better and has lower loss values is then put through one last test. Inception-V3 is therefore selected as the top method for COVID-19 detection. The Inception-V3 system properly predicted each of the normal images and the COVID-19 images in the final test. Regarding pneumonia, it correctly predicted just one image out of 20 that are so small as to be disregarded. When a patient cannot afford to find a doctor for consultation, the DL model created in this work can be utilized as a preliminary test for COVID-19. By including the model created in this study as a backend processor for a website or software application, the study's findings can be updated. © 2023 IEEE.

COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images.

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.

Face Mask Classification based on Transfer Learning and MobileNet V2

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts: with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19. © 2022 ACM.

An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images

COVID-19 is a pandemic and a highly contagious disease that can severely damage the respiratory organs. Tuberculosis is also one of the leading respiratory diseases that affect public health. While COVID-19 has pushed the world into chaos and tuberculosis is still a persistent problem in many countries. A chest X-ray can provide plethora of information regarding the type of disease and the extent of damage to the lungs. Since X-rays are widely accessible and can be used in the diagnosis of COVID-19 or tuberculosis, this study aims to leverage those property to classify them in the category of COVID-19 infected lungs, tuberculosis infected lungs or normal lungs. In this paper, an ensemble deep learning model consisting of pre-trained models for feature extraction is used along with machine learning classifiers to classify the X-ray images. Various ensemble models were implemented and highest achieved accuracy among them was observed as 93%.

Efficient Transfer Learning for COVID-19 Diagnosis using X-Ray and Computed Tomography Images

In this work, we aim to find an effective model to diagnose COVID-19 by using a Transfer Learning (TL) model. The purpose is to classify COVID-19 infected persons from chest X-Ray (XR) and Computed Tomography (CT) images. Several Transfer Learning models have been studied to find the most efficient and effective among them. The proposed approach is based on Tensorflow and the architecture uses the MobileNet_V2 model. The datasets that are used in this study are publicly available. In order to train and evaluate our proposed model, we collected the CT scans dataset of 8000 images with two classes of infected and normal lungs, and the XR dataset contains 616 images. Two experiments are conducted with samples of different sizes to evaluate the model using google colab. The results revealed that the performance of our model MobileNet_V2 is highest with validation accuracy for XR and CT scans images: Val_AccuracyXR =96.77% and Val_AccuracyCT =99.67%, and test time for XR and CT scans images: TXR =0.18s, tCT=0.03s respectively. © 2022 IEEE.

Detection of Sars-Cov-2 from human chest CT images in Multi-Convolutional Neural Network's environment

This article focuses on the detection of the Sars-Cov2 virus from a large-scale public human chest Computed Tomography (CT) scan image dataset using a customized convolutional neural network model and other convolutional neural network models such as VGG-16, VGG-19, ResNet 50, Inception v3, DenseNet, XceptionNet, and MobileNet v2. The proposed customized convolutional neural network architecture contains two convolutional layers, one max pooling layer, two convolutional layers, one max pooling layer, one flatten layer, two dense layers, and an activation layer. All the models are applied on a large-scale public human chest Computed Tomography (CT) scan image dataset. To measure the performance of the various convolutional neural network models, different parameters are used such as Accuracy, Error Rate, Precision, Recall, and F1 score. The proposed customized convolutional neural network architecture's Accuracy, Error Rate, Precision Rate, Recall, and F1 Score are 0.924, 0.076, 0.937, 0.921, and 0.926 respectively. In comparison with other existing convolutional neural network strategies, the performance of the proposed model is superior as far as comparative tables and graphs are concerned. The proposed customized convolutional neural network model may help researchers and medical professionals to create a full-fledged computer-based Sars-Cov-2 virus detection system in the near future. © 2023 IEEE.

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients' chest x-ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest x-ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and support vector machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm, and it was found to have a high classification accuracy of 95%.

Face Mask and Body Temperature Scanning System for Covid-19

Coronavirus illness 2019 has had a major impact on the entire world over the past two to three years. One important approach for people's protection is to wear masks in public. Furthermore, putting on a mask properly Many public service providers demand that users only utilise the service while properly wearing masks. Only a small number of studies have examined face mask identification using image analysis, nevertheless. We suggest Face Mask, a highly accurate and practical face mask detector, in this study. The suggested Face Mask is a one-stage detector that combines a novel context attention module for detecting face masks with a feature pyramid network to fuse high-level semantic information with various feature maps. We also provide a brand-new cross-class object removal method to reject and predictions with a high intersection of union and low confidence. Additionally, we investigate the viability of integrating Face Mask with a portable or embedded neural network called MobileNet. By utilising1)Contactless temperature sensing,2)we create a fack mask detection alarm system to boost COVID-19 indoor safety.Infrared sensor and contactless temperature sensing subsystems rely on Arduino Uno, while computer vision algorithms are used for mask identification. © 2023 IEEE.

Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

Detection of COVID-19 using ResNet50, VGG19, MobileNet, and Forecasting, using Logistic Regression, Prophet, and SEIRD Model

Artificial Intelligence can quickly identify hazardous viral strains in humans. To detect COVID-19 symptoms, AI algorithms can be used to train to examine medical images like X-rays and CT scans. This can help healthcare providers to diagnose the disease more accurately and quickly. AI helps examine data on the spread of COVID-19 andmake predictions about how it will likely spread in the future. Machine learning algorithms known as Convolutional Neural Networks (CNN) are highly effective at evaluating images. As a result, CNN could assist in the early detection of COVID-19 by evaluating medical images like X-rays and CT scans to spot the disease's symptoms. This article's main aim is to provide brief information on some of the CNN models to detect and forecast COVID-19. The models were purely trained with Chest X-ray images of different categorized patients. The COVID-19 prediction models like ResNet50, VGG19, and MobileNet give accuracies of 98.50%, 97.68%, and 93.94%, respectively. On the other hand, forecasting also plays a vital role in reducing the pandemic because it helps us to analyze the risk and plan a solution to avoid it. The model is trained with some forecasting techniques like Prophet, LogisticRegression, and S EIRD model based on a text-based dataset that contains parameters such as the number of people infected per day recovered per day an d many more for visualizing the trends in forecasting, which help in decision-making to analyze risks and plan solutions to prevent the further spread of the disease. © 2023 IEEE.

Comparative Study of Various Lightweight Mask Recognition Algorithms

As an important line of defense against novel coronavirus, masks can effectively reduce the risk of novel coronavirus infection. In this paper, three algorithms were used for mask wear detection, respectively using the opencv native library, MTCNN+MobileNet, and pyramidbox_lite_mobile_mask in paddlehub. Finally, the test results of the three algorithms were analyzed and compared, and the experimental results are that the pyramidbox_lite_mobile_mask model in paddlehub has the most sensitive face recognition and mask detection ability, which can identify the blurred face and judge whether to wear a mask, followed by MTCNN + MobileNet. © 2022 IEEE.

Deep Learning-Based Model for Face Mask Detection in the Era of COVID-19 Pandemic

Recent advancements in the growth of classification tasks and deep learning have culminated in the worldwide success of numerous practical applications. With the onset of COVID-19 pandemic, it becomes very important to use technology to help us control the infectious nature of the virus. Deep learning and image classification can help us detect face mask from a crowd of people. However, choosing the correct deep learning architecture can be crucial in the success of such an idea. This study presents a model for extracting features from face masks utilizing pre-trained models ConvNet, InceptionV3, MobileNet, DenseNet, ResNet50, and VGG19, as well as stacking a fully connected layer to solve the issue. On the face mask 12 k dataset, the study assesses the effectiveness of the suggested deep learning approaches for the task of facemask detection. The performance metrics used for analysis are loss, accuracy, validation loss, and validation accuracy. The maximum accuracy is achieved by DenseNet and MobileNet. Both the models gave a comparable and good accuracies in terms of training and validation (99.89% and 99.79%), respectively. Further, the paper also demonstrates the deployment of deep learning architecture in the real-world using Raspberry Pi 2B (1 GB RAM). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Cognitive Computing in diagnosis of Covid-19 from CT scan and radiograph images: A comparative study

The dreadful coronavirus has not only shattered the lives of millions of people, but it has also placed enormous strain on the whole healthcare system. In order to isolate positive cases and stop the disease from spreading, early detection of COVID-19 is crucial. Currently, a laboratory test (RT-PCR) on samples collected from the throat and nose is required for the official diagnosis of COVID-19. Specialized tools are needed for the RT-PCR test, which takes at least 24 hours to complete. It may often provide more false negative and false positive results than expected. Therefore, using X-ray and CT scan images of the individual's lung, COVID-19 screening can be used to support the conventional RTPCR methods for an accurate clinical diagnosis. The importance of chest imaging in the emergence of this lung illness has been recognized. Images from the computed tomography (CT) scan and chest X-ray (CXR) can be used to quickly and accurately diagnose COVID-19. However, CT scan pictures have their own drawbacks. In order to assess the effectiveness of chest imaging approaches and demonstrates that CXR as an input may compete with CT scan pictures in the diagnosis of COVID-19 infection using various CNN based models, this article thoroughly covers modern deep learning techniques (CNN). For CXR and CT scan pictures, we have evaluated with ResNet, MobileNet, VGG 16, and EfficientNet. Both chest X-ray (3604 Images) and CT scans (3227 images) from publicly accessible databases have been evaluated, and the experimental outcomes are also contrasted. © 2022 IEEE.

Analysis on Chest X-ray and Computed Tomography Images to Detect COVID-19 Based on Deep Learning Techniques

More than 6.3 million individuals have died as a result of the Corona Virus Disease 2019 (COVID-19), which spoiled many more human health globally. Since COVID-19 is a pandemic that is rapidly spreading, early discovery is essential to halting the infection's spread. Images of the lungs are utilised to identify coronavirus infection. For the identification of Corona Virus Disease, chest X-ray (CXR) and computed tomography (CT) images are available. Deep learning methods are proved to be effective and perform better in medical imaging applications. This study examines lung CT pictures, classifies and segments them, and uses the results to identify whether a patient tested is affected by COVID-19 or not using Deep learning techniques. The COVID detection performance of the deep learning architectures GG19, MobileNet, COVID-Net (PEPX), Squeez Net, U-Net, DarkNet and VGG16 are analysed - it was shown that U-Net combined VGG16 (acc98.89%) and VGG19 (acc-98.05%) performs the best, followed by MobileNet and QueezNet. © 2022 IEEE.

Cough-based COVID-19 Detection with Multi-head Deep Neural Network

Clinicians have long used audio signals created by the human body as indications to diagnose sickness or track disease progression. Preliminary research indicates promise in detecting COVID-19 from voice and coughing acoustic signals. In this paper, various popular convolutional neural networks (CNN) are employed to detect COVID-19 from cough sounds available in the Coughvid opensource dataset. The CNN models are given input in the form of hand-crafted features or raw signals represented using spectrograms. The CNN architectures for both the types of inputs has been optimized to enhance performance. COVID-19 could be detected from cough sounds with an accuracy of 77.5% using CNN on handcrafted features, and 72.5% using VGG16 on spectrograms. However, result show that the concatenation of the two in a multi-head deep neural network yield higher accuracy as compared to just using hand-extracted features or spectrograms of raw signals as input. The classification improved to 81.25% when ResNet50 was employed in the multi-head deep neural network, which was higher than that obtained with VGG16 and MobileNet. © 2022 IEEE.

COVID Detection Using Chest X-ray Images Using Ensembled Deep Learning

COVID-19 originated in Wuhan, China, in December 2019, and there have been over 464.5 million infected cases, and 6.08 million individuals have died worldwide. Effective detection of COVID-19 has been an essential task for stopping its quick spread and ultimately saving precious lives. This paper considers radiological examination using chest X-rays as patients with COVID-19 infections are likely to be adequately recognized using chest radiography pictures. Although many machine learning/deep learning techniques have been developed, their approach is likely to suffer problems like generalization error, high variance, overfitting, etc., due to limited dataset size. By producing predictions with numerous models rather than only one model, the ensemble model can overcome the disadvantages of deep learning. So, in this paper, we propose an ensemble deep learning method for detecting COVID-19 using chest X-ray images. On a combination of DenseNet, InceptionV3, and MobileNet, we got the best validation accuracy of 96.20% and testing accuracy of 92.45%. We hope this approach will help detect COVID-19 early and reduce further spread. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Access Control Through Mask Detection and Estimation of People Capacity in Covered Premises

The health emergency due to the COVID-19 pandemic requires the search for technological and intelligent solutions that facilitate the control of biosecurity measures such as social distancing, to use of a mask, and capacity in covered spaces. This work aims to develop a prototype based on artificial vision algorithms, capable of performing the automatic mask detection and people counting who go to covered premises such as bars, restaurants, gyms, cinemas, and micro-market among others. The prototype implements SSD-MobileNet object detection and SORT tracking algorithms that work on the electronic device NVIDIA Jetson Nano, equipped with two video cameras to perform mask detection and people counting respectively, as well as speakers, for emission of audible alert messages about the use of mask and the capacity estimation within the premise and an external web server too in which people counter information is displayed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Detection Based Entery

The Covid-19 pandemic has introduced several challenges to the society and safety measures are of utmost importance. Hence, to contain and reduce the spread, mask detection-based entry has emerged as a very fascinating topic in the domains of image processing, computer vision, and the Internet of Things (IoT). Convolutional architectures are being used to develop a number of new algorithms that will improve the accuracy of the algorithm. Such convolutional architectures have also made possible the extraction of pixel details. The project aims to build a binary face classifier which can detect the presence of a mask and accordingly people will be granted entry. The classifier is created by using Convolutional Neural Network (CNN), Region based CNN (R-CNN), ThingSpeak. © 2022 IEEE.

Real-time Face mask Detection Using Deep Learning and MobileNet V2

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning-Driven Medical Imaging Analysis for COVID-19 Detection

The primary requirement for early detection of COVID and control of the virus's spread is rapid and precise diagnosis. Computer vision and deep learning-based models can be used to assist this COVID diagnosis process through chest X-ray scans. This study performs a comparative analysis on different deep learning models which can be used to diagnose COVID from chest X-ray scans. For this work, the following deep learning models were selected: VGG16, Xception, ResNet, DenseNet, and MobileNet. This research looks not only at COVID, but also at other SARS-CoV-2-related diseases such as SARS and MERS. The dataset used consists of five categories: normal, COVID, pneumonia, SARS, and MERS. The comparative study showed that both the MobileNet and DenseNet models were able to deliver the best performance, with the highest accuracy and minimal loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.