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.

Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images

The 2019 coronavirus (COVID-19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre-trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X-ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre-trained models such as ResNet-50, Inception V3, VGG-16, VGG-19, ResNet50V2, and Xception are used for better feature extraction and Chest X-ray image classification. The overfitting issue were resolved using K-fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.

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.

Securing health care data through blockchain enabled collaborative machine learning.

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

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.

Dhanvantari: An intelligent diagnosis tool to classify malignant skin disease and Lung conditions using Deep Learning

The pandemic due to COVID-19 has created a huge gap in the medical field leading to a reduction in the efficacy of this field. To improve this situation, we propose a solution 'Dhanvantari'. A medical app that is powered by Artificial Intelligence performs a task where the diagnosis is done by computer vision observing CT scans, MRIs, and also some skin diseases. Dhanvantari focuses mainly on the combination of CT scans and skin disease classifications. In this paper, a novel approach has been proposed for developing a supervised model for the classification of skin disease and lung ailments (that is to identify a healthy lung with an infected lung due to pneumonia) through analog to digital image processing. This app helps the user in analyzing conditions and if any abnormalities are detected then alerts the user about it. This is a primary service care application developed to reduce the number of false cases hence only alerting the user if a complication is observed. The proposed approach utilizes a camera and computational device or mobile. Two datasets from Kaggle that had 9 classes of malignant skin disease and 2 lung conditions were used to train the model. Design, training, and the testing of the algorithm were performed with the help of colab. Generally, a standard test for malignant skin disease requires sample gathering and conduction of various tests. All these consume a lot of time. The other method is laser or radiation-induced procedures that might be harmful and lead to exposure of unwanted radiation to patients. The proposed 'Dhanvantari' requires the patient/user to use a camera to take a picture of the affected area (in case of skin condition) and it provides the primary diagnosis. This approach aids the doctors in quick decision-making during diagnosis and reduce the time per patient which in house helps them to prioritize patients. © 2022 IEEE.

Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans

Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better. © 2022 IEEE.

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification.

OBJECTIVES: The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications. METHODS: The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells. RESULTS: After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%. CONCLUSIONS: Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.

Driver drowsiness detection and smart alerting using deep learning and IoT

Drowsiness is a common problem that many drivers encounter due to long working hours, lack of sleep, and tiredness. Tired drivers are as dangerous as drunk drivers because they have slower reaction times and suffer from reduced attention, awareness, and ability to control their vehicles. Drowsy driving causes many traffic accidents, especially fatal crashes. Therefore, the best way to prevent accidents involving drowsiness is to alert the drivers ahead of time. The accuracy of the drowsiness prediction reduces if the studies only focus on facial landmarks, ignoring other fatigue features such as tilting head, blinking, and yawning. To solve these problems, we propose an approach to detect driver drowsiness efficiently and accurately using IoT and deep neural networks improved from LSTM, VGG16, InceptionV3, and DenseNet. The use of transfer learning technique combined with multiple drowsiness signs is to improve the accuracy of the drowsiness detection in various driving conditions. The time-varying factor is also taken into consideration in the models developed from LSTM and DenseNet. When the driver's fatigue is detected, the IoT module emits a warning message along with a sound through a Jetson Nano monitoring system. The experimental results demonstrate that our approach using deep neural networks can achieve high accuracy of up to 98%. Notably, this approach has also been verified in cases with/without wearing a mask and glasses. This has a practical meaning in the Covid-19 pandemic situation when everyone needs to comply with the wearing of masks in public places.

Energy- efficient model "Inception V3 based on deep convolutional neural network" using cloud platform for detection of COVID-19 infected patients

Objectives: COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy. Methods: We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification. Results: Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays. Conclusions: In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper. © 2023 Walter de Gruyter GmbH. All rights reserved.

Artificial Intelligence for the Detection of Covid-19 Pneumonia on Chest X-Ray

According to earlier studies, chest x-ray (CXR) pictures might be used as a preventative measure for artificial intelligence (AI) to identify corona pneumonia. Unfortunately, problems with the collections and research approaches from a scientific and medical standpoint have also surfaced, and we also have concerns about the resilience and susceptibility of AI systems. In this work, we solve these concerns by creating our original information through some kind of retroactive preclinical studies to supplement the data gathered from external factors, leading to a more accurate generation of AI-driven coronavirus detection techniques. In order to objectively analyse research strategy, we created design techniques by modifying datasets, optimised five learning algorithms, and added a variety of sensing circumstances to assess the resilience and analytical effectiveness of the methods. In a 3-class identification situation as opposed to a 4-class research design, the study demonstrates superior overall efficiency of 91-96 percent Sn (sensibility), 94-98 percent Sp (precision), and 90-96 percent PPV. With a reliability score of F1-measure, as well as a g-average of 96% in the 3 class instances of identification, InceptionV3has the best overall result. At the same time, InceptionV3exhibited the best results for COVID-19 pneumonia identification with 86 percent Sn, 99 percent Sp, and 91 percent PPV with an AUC of 0.99 in separating pneumonia with regular CXR. It achieved 0.98 AUC and a stutter stepping mean of 0.99 for those other categories for its capability to distinguish between COVID-19 pneumonia and non-COVID-19 pneumonia. © 2022 IEEE.

Artificial Intelligence for the Detection of Covid-19 Pneumonia on Chest X-Ray

According to earlier studies, chest x-ray (CXR) pictures might be used as a preventative measure for artificial intelligence (AI) to identify corona pneumonia. Unfortunately, problems with the collections and research approaches from a scientific and medical standpoint have also surfaced, and we also have concerns about the resilience and susceptibility of AI systems. In this work, we solve these concerns by creating our original information through some kind of retroactive preclinical studies to supplement the data gathered from external factors, leading to a more accurate generation of AI-driven coronavirus detection techniques. In order to objectively analyse research strategy, we created design techniques by modifying datasets, optimised five learning algorithms, and added a variety of sensing circumstances to assess the resilience and analytical effectiveness of the methods. In a 3-class identification situation as opposed to a 4-class research design, the study demonstrates superior overall efficiency of 91-96 percent Sn (sensibility), 94-98 percent Sp (precision), and 90-96 percent PPV. With a reliability score of F1-measure, as well as a g-average of 96% in the 3 class instances of identification, InceptionV3has the best overall result. At the same time, InceptionV3exhibited the best results for COVID-19 pneumonia identification with 86 percent Sn, 99 percent Sp, and 91 percent PPV with an AUC of 0.99 in separating pneumonia with regular CXR. It achieved 0.98 AUC and a stutter stepping mean of 0.99 for those other categories for its capability to distinguish between COVID-19 pneumonia and non-COVID-19 pneumonia. © 2022 IEEE.

Energy- efficient model "Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy.We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification.Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays.In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3 model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper.

Multiclass Classification of COVID-19, Pneumonia, or Nornal Lungs Based on Chest X-Ray Images with Ensemble Deep Learning

Coronavirus Disease of 2019 (COVID-19) has a high transmission and death rate. It is important to diagnose COVID-19 accurately and distinguish it clearly from other common lung diseases, e.g., pneumonia. Both diseases are detectable from chest X-Ray images. Therefore, an ensemble deep learning model is applied for multiclass classification of COVID-19, pneumonia, or normal lungs based on chest X-Ray images. ResNet50, VGG16, and InceptionV3 pretrained CNN models are employed to form an ensemble model. The chest X-Ray images are preprocessed in three steps, i.e., cropping, resizing, and normalization. Then, the pretrained models are trained with a new classifier at the top layer of the model. After the classifier is trained, then the pretrained ResNet50, VGG16, and InceptionV3 are fine-Tuned. Lastly, the decisions from each model are assembled using Soft Voting. The ensemble deep learning model which produces the best result, which is formed by combining pretrained and fine-Tuned ResNet50, VGG16, and InceptionV3 models, results weighted accuracy of 0.9752, weighted sensitivity of 0.9612, and weighted specificity of 0.9804. © 2022 IEEE.

Multi-class Classification of COVID-19 in Chest X-Ray Using Deep Convolutional Neural Networks

COVID-19 has principally affected everybody within the world in a way or another and thousands of individuals are becoming infected daily. In Present ways for checking COVID positive or negative, is taking a lot of time for results and these results are giving low specificity and sensitivity. Because of that the computer science—Artificial Intelligence (AI) is necessary in finding the positive COVID-19 cases. With Image processing and machine learning and deep learning techniques the researchers are able to achieve high accuracy and sensitivity and specificity from Chest X-ray (CXR) and Computed tomography (C.T) images. In this paper, we have proposed different deep neural networks like CNN, Alexnet, ResNet, Inception-v3 and ResNeXt-101-32x8d (all of those belong to the CNN family) with around 20,000+ CXR pictures of 3 classes. CXR is the initial technique which is important in diagnosing the Covid-19 patients. For verifying the strength of the models we compared validation accuracies, inception V3 achieved the best accuracy of 95%, however, we must always conjointly take into account the training time and complexity of the model. When the models accuracy, specificity, and sensitivity are higher, then it is really helpful for non-radiologist medical staff to diagnoses and quarantine faster when hospitals are flooded with patients, It reduces screening time for COVID-19 greatly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 Detection Based-On CT-Scan Images Using Inception Deep Learning

The SARS-Cov-2 strain caused COVID-19, inflicting mild to moderate respiratory problems. The spread of COVID-19 is extremely fast which has resulted in the number of victims who have been declared dead to date, up to 2,587,225. There are several ways to reduce the spread of COVID-19, one of which is early detection. Currently, there are alternative methods used for early detection, one of which is the neural network method. Deep learning is one type of artificial neural network that is often used for the detection of several kinds of diseases. In this study, we classify CT-Scan images of the lungs based on two classes, namely CT-COVID and CT-NonCOVID, using two models, Inception-v3 and Inception-v4. The total CT-Scan image data used is 2038 and comes from the Kaggle.com website. Results obtained were then compared with standard performance metrics and then analyzed between the best models among the models used in the COVID-19 classification. From the results of the study, the Inception-v3 model obtained an average accuracy value of 93.96%, a precision value of 90.57%, a recall value of 95.65%, a specificity value of 92.81% and an f-score value of 92.51% and The Inception-v4 model obtained an average accuracy value of 86.41%, a precision value of 77.01%, a recall value of 91.18%, a specificity value of 83.77% and an f-score value of 83.38%. Based on the research results, the method with the best performance in COVID-19 classification is the Inception-v3 model because the Inception-v3 model has more layers, with a total of 48 layers and utilizes the idea of factorization that is more suitable for CT-Scan image classification which has low contrast visualization. © 2022 IEEE.

Diagnosis of Covid-19 using Deep Learning

In the global health disaster of the Coronavirus infection-2019 (Covid-19) pandemic, the health sector is avidly seeking new technologies and strategies to detect and manage the spread of the Coronavirus outbreak. Artificial Intelligence (AI) is currently one of the most essential aspects of global technology since it can track and monitor the rate at which the Coronavirus develops as well as determines the danger and severity of Coronavirus patients. In this paper, we have proposed a two-stage end-to-end Deep Learning (DL) model which can be used to predict the presence and severity of Covid-19 infection in a patient as early and accurately as possible so that the spread of this viral infection can be slowed down. Hence, based on the Computed Tomography (CT) scans or chest X-rays provided by the user as an input, the DL models are built that can forecast the presence of Covid-19 in that respective patient accurately and efficiently. In this paper, 5 DL models i.e., VGG16, InceptionV3, Xception, ResNet50, and Convolution Neural Networks (CNN) are built and their comparative analysis is carried out for the diagnosis of Covid-19. On the Google Colab GPU, the models are trained for 100 epochs on a total of 1686 images of chest X-rays and CT scans. The experimental results show that out of all these models, the model based on the Xception algorithm is the most accurate one in determining the presence of the disease and provides an accuracy of 81% and 89% on CT scans and Chest x-rays respectively. © 2022 ACM.

Deep Neural Network models for diagnosis of COVID-19 Respiratory diseases by analyzing CT-Scans and Explain-ability using trained models

The Novel Coronavirus, popularly known as "COVID-19,"is causing a pandemic over the world. This virus causes severe respiratory disease in those who are afflicted. Symptoms such as fever, dry cough, and exhaustion can be used to identify this virus.These symptoms, on the other hand, are comparable to those of other viral or respiratory illnesses. There is no quick method to tell whether or not someone has been exposed to the virus.. To counter the aforementioned constraints, a quicker diagnosis is desired, which brings us to the study's goal: to develop a diagnostic approach that incorporates previous data, mostly from COVID-19, as well as data-sets from other respiratory disorders. Deep learning models will be used to evaluate the data sets we've gathered, helping us to make more accurate and efficient decisions. convolutional Neural Network models such as VGG 19, Inception v3, MobileNet V2, and ResNet 50 are among the Deep Neural Network models we plan to deploy. These four models have been pre-trained to categorize CT-Scan images using trained learning methodologies. To obtain faster and more accurate answers, the outcomes of each model are compared among the models. A "Hybrid"model built of Convolutional Neural Network and a Support Vector Machine is also proposed in this research. The Hybrid Model is not as deep as the pre-trained models, but it is as accurate. We will be able to diagnose more correctly and effectively based on the correctness of the outcome and the shortest time necessary for categorization of images which will enable us to diagnose more accurately and effectively.In our research work we have collected data-sets from git-hub [19] and Kaggle and in total we have gathered 877 images of chest X-rays and CT-scans. We operated data-augmentation and smote analysis on our data-set. After training and testing our models we have obtained the following accuracy scores: 0.9384 validation-accuracy and 0.9361 train-accuracy for Hybrid model, 0.9806 validation-accuracy and 0.9692 train-accuracy for Inception-V3, 0.9806 validation-accuracy and 0.9846 train-accuracy for MobileNet V2, 0.7206 validation-accuracy and 0.6705 train-accuracy for Resnet 50, 0.9107 validation-accuracy and 0.9685 train-accuracy for VGG 19. © 2022 IEEE.

Diagnosing Covid-19 and Pneumonia from Chest CT-Scan and X-Ray Images Using Deep Learning Technique

The outbreak of Covid-19 (COV-19) has become one of the global severe public health issues. It is an ongoing global pandemic that has spread rapidly. It not only affected human beings but also paralyzed most industries. It is very critical to diagnose COV-19 & Pneumonia (PNA) because both are having the same sign & symptoms that correlate to the most extent. The (RT-PCR) Reverse Transcription- Polymerise Chain Reaction widely used official screening method for detection of COV-19, while radio-logical imaging CT Scan (Computed Tomography) of human chests is used for detection of PNA as well as for COV-19 diagnosis also. The method for detecting COV-19 & PNA using CXR (Chest X-ray) & CT scans is too time consuming for an expert while the result accuracy is less. In this proposed project the work is based on the transfer learning model utilizing the Deep learning module for COV-19 & PNA solicitation from radiological images of the patient suffering from PNA & COV-19 which plays critical role in early detection & classification of COV-19 & PNA. In the present work multi-model deep learning modules are used for the image detection are as follows: DenseNet 121, MobileNet, Inception V3, ResNet 50, & VGG 16. In this study, the data set contains X-Ray & CT-Scan Images which had been collected from CORD-19 & PNA Data sets. To evaluate the effect of dataset size based on the performance of multi-modal deep learning modules, to train the proposed module of deep learning using both the original & augmented dataset, the result were quite promising for DenseNet-121 for CT Scan with 97% accuracy while VGG 16 for CXR images with 99% accuracy. © 2022 IEEE.

A Modern Replica for COVID-19 Pestilential Disease Identification

Detection of COVID-19 disease and its unmasking, demands a certain level of proficiency. The Work exhibited in the paper proposes a novel Deep Learning based approach to recognize COVID-19 contagious infection using CT scans and X- Rays of lungs in Humans. So that labour and risk intensive task for radiotherapists of taking samples from the patients can be minimized and risk of community spread can be avoided. Our model takes into the CT scan chest images of the patient having a certainty of infection and returns the most significant disease category related to that patient. In our study, we demonstrated a Deep Learning framework model that follows the methodology of up-skilled feature extraction techniques along with Logistic Regression [LR] and other usable classifiers. This is used on images to detect and report the presence of infection that is being prevailed in an organ with a considerably pinpoint accuracy of 97.8%. Also after trying the model on spatial information real- time dataset of our Family members, who were infected by the disease, this model was able to detect 8 out of 10 images correctly. © 2022 IEEE.