Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

Towards Lifting the Trade-off between Accuracy and Adversarial Robustness of Deep Neural Networks with Application on COVID 19 CT Image Classification and Medical Image Segmentation

Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general strategy to improve DNN robustness. But training a DNN model with adversarial noises may result in a much lower accuracy on clean data, which is termed the trade-off between accuracy and adversarial robustness. Towards lifting this trade-off, we propose an adversarial training method that generates optimal adversarial training samples. We evaluate our methods on PathMNIST and COVID-19 CT image classification tasks, where the DNN model is ResNet-18, and Heart MRI and Prostate MRI image segmentation tasks, where the DNN model is nnUnet. All these four datasets are publicly available. The experiment results show that our method has the best robustness against adversarial noises and has the least accuracy degradation compared to the other defense methods. © 2023 SPIE.

Bayesian optimization for artificial neural networks: application to Covid-19 image classification

Training a Convolutional Neural Network (CNN) is a difficult task, especially for deep architectures that estimate a large number of parameters. Advanced optimization algorithms should be used. Indeed, it is one of the most important steps to reduce the error between the ground truth and the model prediction. In this sense, many methods have been proposed to solve the optimization problems. In general, regularization, more specifically, non-smooth regularization, can be used in order to build sparse networks, which make the optimization task difficult. The main aim is to develop a novel optimizer based on Bayesian framework. Promising results are obtained when our optimizer is applied on classification of Covid-19 images. By using the proposed approach, an accuracy rate equal to 94% is obtained surpasses all the competing optimizers that do not exceed an accuracy rate of 86%, and 84% for standard Deep Learning optimizers. © 2022 IEEE.

Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

Biomedical Image Classification Using Convolutional Neural Networks

Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life. © 2023 IEEE.

Predicting COVID-19 from Chest X-ray Images using a New Deep Learning Architecture

The spread of coronavirus disease in late 2019 caused huge damage to human lives and forced a chaos in health care systems around the globe. Early diagnosis of this disease can help separate patients from healthy people. Therefore, precise COVID-19 detection is necessary to prevent the spread of this virus. Many artificial intelligent technologies for example deep learning models have been applied successfully for this task by employing chest X-ray images. In this paper, we propose to classify chest X-ray images using a new end-To-end convolutional neural network model. This new model consists of six convolutional blocks. Each block consists of one convolutional layer, one ReLU layer, and one max-pooling layer. The new model was applied on a challenging imbalanced COVID19 dataset of 5000 images, divided into two classes, COVID and Non-COVID. In experiments, the input image is first resized to 256×256×3 before being fed to the model. Two metrics were used to test our new model: sensitivity and specificity. A sensitivity rate of 97% was achieved along with a specificity rate of 99.32%. These results are promising when compared to other deep learning models applied on the same dataset. © 2022 IEEE.

Real Time Mask Detection using Convolutional Neural Network

With the advent of Convolutional Neural Networks, the field of image classification has seen tremendous growth, with various previously impossible applications now being pursued. One such application is face mask detection, which is an important problem to solve, considering recent pandemic. The novelty of this work is the training of YOLO (You Only Look Once) framework for custom object detection, which in this case is face mask, based on some empirical rules for fine-tuning the performance. Also, image classification is proposed to be combined with tracker, in order to implement real world access grant system based on compliance shown by mask wearer. © 2022 IEEE.

Lung Lesion Images Classification Based on Deep Learning Model and Adaboost Techniques

Today, the medical industry is promoting the research and application of artificial intelligence in disease diagnosis and treatment. The development of diagnostic methods with the support of electronic devices and information technology can help doctors save time in diagnosing and treating diseases, especially medical images. Diagnosis of lung lesions based on lung images is a case study. This paper proposed a method for lung lesion images classification based on modified U-Net and VGG-19 combined on adaboost techniques. The modified U-Net architecture with 5 pooling and 5 unpooling. It has the unpooling layer with kernels of size 2 × 2, stride 2 × 2 to get output consistent with the adaboost. The result of the proposed method is about 97.61% and better results than others in the Covid-19 radiography dataset. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Detecting COVID using CNN from Chest X-Beams

Since inception of Corona Virus, 47.6 Cr. individuals got infected and 61L deaths occurred. Still it's going on and spreading across the world. Many health workers, researchers, experts, scientists are making efforts to slow down its pace & putting efforts in evaluating the techniques to detect it. For this, it is highly required to understand the virus & its versions. It is a part of SARS - Severe acute respiratory syndrome. To detect COVID, there are numerous ways but using Chest X-beams we are able to reduce the detection time and cost. To evaluate the Chest X-beams we need radiologists. So here, we develop a model to identify COVID X-beam in comparison to Normal X-beam. These days DL algo's are producing best results in classification. A pre-trained CNN models using large datasets is to preferred for image classification. Firstly our models need to be trained and then tested to recognize the images of X-beams of one of the either case. Logically we have to locate the best CNN model for diagnosis. © 2022 IEEE.

Face Mask Detection Using YOLOv3

The COVID-19 is an unprecedented crisis that has resulted in several security issues and large number of casualties. People frequently use masks to protect themselves against the transmission of coronavirus. In view of the fact that specific aspects of the face are obscured, facial identification becomes extremely difficult. During the ongoing coronavirus pandemic, researchers' primary focus has been to come up with suggestions for dealing with the problem through rapid and efficient solutions, as mask detection is required in the current scenario, whether in public or in some institutions such as offices and other workplaces. Only detecting whether a person wears mask or not is not enough. There is another aspect of wearing the mask properly such that it covers all the required portion of the face to ensure there is no exposure to any viruses. To address this, we proposed a reliable technique based on image classification and object localization, which can be accomplished using YOLO v3's object detection in machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Improving ransomware detection based on portable executable header using xception convolutional neural network

All malware are harmful to computer systems;however, crypto-ransomware specifically leads to irreparable data loss and causes substantial economic prejudice. Ransomware attacks increased significantly during the COVID-19 pandemic, and because of its high profitability, this growth will likely persist. To respond to these attacks, we apply static analysis to detect ransomware by converting Portable Executable (PE) header files into color images in a sequential vector pattern and classifying these via Xception Convolutional Neural Network (CNN) model without transfer learning, which we call Xception ColSeq. This approach simplifies feature extraction, reduces processing load, and is more resilient against evasion techniques and ransomware evolution. The proposed method was evaluated using two datasets. The first contains 1000 ransomware and 1000 benign applications, on which the model achieved an accuracy of 93.73%, precision of 92.95%, recall of 94.64%, and F-measure of 93.75%. The second dataset, which we created and have made available, contains 1023 ransomware, grouped in 25 still active and relevant families, and 1134 benign applications, on which the proposed method achieved an accuracy of 98.20%, precision of 97.50%, recall of 98.76%, and F-measure of 98.12%. Furthermore, we refined a testing methodology for a particular case of zero-day ransomware attacks detection—the detection of new ransomware families—by adding an adequate amount of randomly selected benign applications to the test set, providing representative evaluation performance metrics. These results represent an improvement over the performance of the current methods reported in the literature. Our advantageous approach can be applied as a technique for ransomware detection to protect computer systems from cyber threats. © 2023 Elsevier Ltd

Comparitive Analysis on Neural Networks based on their performance in Pneumonia Detection

Pneumonia has been a concerning issue worldwide. This infectious disease has a higher mortality rate than Covid-19. More than two million individuals lost their lives in 2019 out of which almost 600,000 were infants less than 5 years of age. Globally, identification of the disease is done manually by radiologists, but this method is highly unreliable as its accuracy is not sufficiently good. With the evolution of computational resources, especially the computing power of GPUs, it has become possible to train very deep CNNs. This study involves a comparative analysis of neural networks for pneumonia recognition. The goal is to do binary image classification for pneumonia recognition on each of the three models, namely, a Sequential model using TensorFlow (built from scratch), ResNet50 and InceptionV3 and comparing their efficiency, to discover which model suits best for smaller datasets and which suits best for larger datasets. Dataset consists of 5856 anterior and posterior Chest X-Ray images labeled as either Normal or Pneumonic. © 2023 IEEE.

Applied Enhanced Q-NAS for COVID-19 Detection in CT Images

The Deep Neural Networks are flexible and robust models that have gained attention from the machine learning community over the last decade. During the construction of a neural network, an expert can spend significant time designing a neural architecture with trial and error sessions. Because of the manual process, there is a greater interest in Neural Architecture Search (NAS), which is an automated method of architectural search in neural networks. Quantum-inspired evolutionary algorithms present propitious results regarding faster convergence when compared to other solutions with restricted search space and high computational costs. In this work, we enhance the Q-NAS model: a quantum-inspired algorithm to search for deep networks by assembling substructures. We present a new architecture that was designed automatically by the Q-NAS and applied to a case study for COVID-19 vs. healthy classification. For this classification, the Q-NAS algorithm was able to find a network architecture with only 1.23 M parameters that reached the accuracy of 99.44%, which overcame benchmark networks like Inception (GoogleLeNet), EfficientNet and VGG that were also tested in this work. The algorithm is publicly avaiable at https://github.com/julianoce/qnas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder with Embedding Space for Covid-19 Image Classification

In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. This model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer's multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. This model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets. © 2022 IEEE.

Explainability of deep learning models in medical image classification

The ability to explain the reasons for one's decisions to others is an important aspect of being human intelligence. We will look at the explainability aspects of the deep learning models, which are most frequently used in medical image processing tasks. The Explainability of machine learning models in medicine is essential for understanding how the particular ML model works and how it solves the problems it was designed for. The work presented in this paper focuses on the classification of lung CT scans for the detection of COVID-19 patients. We used CNN and DenseNet models for the classification and explored the application of selected visual explainability techniques to provide insight into how the model works when processing the images. © 2022 IEEE.

Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality Due to Infectious Disease

Analyzing chest X-ray is the must especially when are required to deal of infectious disease outbreak, and COVID-19. The COVID-19 pandemic has had a large effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans might not be able to. This allows computers to learn on relatively little data and provide exceptional results. This paper proposes a customized convolutional neural network (CNN) for the detection of COVID-19 from chest X-rays called basicConv. This network consists of five sets of convolution and pooling layers, a flatten layer, and two dense layers with a total of approximately 9 million parameters. This network achieves an accuracy of 95.8%, which is comparable to other high-performing image classification networks. This provides a promising launching point for future research and developing a network that achieves an accuracy higher than that of the leading classification networks. It also demonstrates the incredible power of convolution. This paper is an extension of a 2022 Honors Thesis (Henderson, Joshua Elliot, "Convolutional Neural Network for COVID-19 Detection in Chest X-Rays” (2022). Honors Thesis. 254. https://red.library.usd.edu/honors-thesis/254 ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Medical Images Classification Using Hybrid Deep Learning CNN-PSO-GA

Convolutional Neural Network (CNN) has made outstanding achievements in image processing and detection. The recent research uses CNN to classify the medical images, but this performance depends on its hyperparameters chosen by the programmer. Choosing these parameters is a difficult process if done manually, so there is a need to find out alternative methods. To solve this problem, the researchers hybridized a CNN with particle swarm optimization (PSO) to find better values for these hyperparameters. PSO was hybridized using genetic algorithm to solve the retired particle problem. The purpose of this research is to take advantage of the achievements of deep learning in classifying medical images. The proposed model was tested with three datasets: malaria, COVID-19, and pneumonia. The model achieved 99.5%, 100%, and 99.7% accuracy for the above datasets respectively. These results were compared with the results of the standard CNN;the proposed model surpassed the standard CNN in overall performance. © 2022 IEEE.

Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection

COVID-19 cases attract most computer science researchers. There are two popular learning approaches: Machine Learning (ML) and Deep Learning (DL). The approach was applied as a computer-based COVID-19 diagnosis. Most researchers prefer ensemble learning used to assist the process. The technique has various features and performance results. Based on the survey, there are several efforts to improve performance better. This review describes a brief of the ensemble approach. The ensemble applies to image classification. The application employs X-Ray and Computerized Tomography (CT) images. The technique should consider various ensemble strategies. As supportive evidence, a brief description of each method is presented in the table. This study shows all ensemble methods demonstrate to improve prediction results. The stacking ensemble becomes a method that achieves the highest performance. © 2022 IEEE.

Research on CT image classification algorithm of COVID-19 based on improved ResNet

The classification of COVID-19 and other viral pneumonias will help doctors to diagnose new coronary patients more accurately and quickly. Aiming at the classification problem of CT in patients with COVID-19, this paper proposes a CT image classification method based on an improved ResNet50 network based on the traditional convolutional neural network classification model. This paper uses the multiscale feature fusion strategy, combined with the improved attention mechanism to obtain the correlation coefficient between the internal feature points of the feature map, and finally achieves the effect of enhancing the representation ability of the feature map. Through the analysis and comparison of the technical principle, classification accuracy, and other parameters, it shows that the improved algorithm has better adaptive ability and classification ability. Through experiments, the improved ResNet50 classification model has a certain improvement in accuracy, time complexity, and spatial complexity compared with the traditional classification model, and the accuracy rate can reach 90.1 %. © 2022 Copyright for this paper by its authors.