Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Classification of pneumonia from X-ray images using siamese convolutional net...TELKOMNIKA JOURNAL
Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images.
Automatic COVID-19 lung images classification system based on convolution ne...IJECEIAES
Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
Lung Cancer Detection using Convolutional Neural NetworkIRJET Journal
The document presents a study on detecting lung cancer using convolutional neural networks. Specifically, it uses the YOLO framework to accurately detect lung tumors and their locations in CT images. The proposed system first collects CT images and pre-processes them before training a YOLO object detection model. The trained model is then used to detect and localize tumors in test images and provide classification. Evaluation shows the model can successfully pinpoint tumors attached to blood vessels and distinguish between different types of lung cancer. The authors aim to improve the model through expanding the dataset and exploring updated deep learning techniques.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary ...IJECEIAES
Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
Preliminary Lung Cancer Detection using Deep Neural NetworksIRJET Journal
This document presents a study on using deep learning techniques for preliminary lung cancer detection. Specifically, it proposes using a convolutional neural network (CNN) model for classifying histopathological lung cancer tissue images. The study describes the dataset used, which contains labeled RGB images of cancerous and non-cancerous lung tissue. It then discusses the proposed CNN architecture, which includes convolutional, pooling, dropout and fully connected layers. The model is trained on the dataset for 30 epochs and achieves 96.43% accuracy on the training set and 97.10% accuracy on the validation set, indicating it generalizes well for lung cancer classification. In conclusion, the CNN model shows promising results for preliminary lung cancer detection from histopathological images.
This study combines gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 and normal chest X-rays. 600 chest X-ray images were used, with 300 normal and 300 COVID-19 cases. GLCM extracted texture features, and a neural network classified the images. Testing different epochs, the model achieved 91-92% accuracy. Combining GLCM feature extraction and neural network classification produced good results for identifying COVID-19 in chest X-rays.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Classification of pneumonia from X-ray images using siamese convolutional net...TELKOMNIKA JOURNAL
Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images.
Automatic COVID-19 lung images classification system based on convolution ne...IJECEIAES
Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
Lung Cancer Detection using Convolutional Neural NetworkIRJET Journal
The document presents a study on detecting lung cancer using convolutional neural networks. Specifically, it uses the YOLO framework to accurately detect lung tumors and their locations in CT images. The proposed system first collects CT images and pre-processes them before training a YOLO object detection model. The trained model is then used to detect and localize tumors in test images and provide classification. Evaluation shows the model can successfully pinpoint tumors attached to blood vessels and distinguish between different types of lung cancer. The authors aim to improve the model through expanding the dataset and exploring updated deep learning techniques.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary ...IJECEIAES
Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
Preliminary Lung Cancer Detection using Deep Neural NetworksIRJET Journal
This document presents a study on using deep learning techniques for preliminary lung cancer detection. Specifically, it proposes using a convolutional neural network (CNN) model for classifying histopathological lung cancer tissue images. The study describes the dataset used, which contains labeled RGB images of cancerous and non-cancerous lung tissue. It then discusses the proposed CNN architecture, which includes convolutional, pooling, dropout and fully connected layers. The model is trained on the dataset for 30 epochs and achieves 96.43% accuracy on the training set and 97.10% accuracy on the validation set, indicating it generalizes well for lung cancer classification. In conclusion, the CNN model shows promising results for preliminary lung cancer detection from histopathological images.
This study combines gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 and normal chest X-rays. 600 chest X-ray images were used, with 300 normal and 300 COVID-19 cases. GLCM extracted texture features, and a neural network classified the images. Testing different epochs, the model achieved 91-92% accuracy. Combining GLCM feature extraction and neural network classification produced good results for identifying COVID-19 in chest X-rays.
This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
A Review Paper on Covid-19 Detection using Deep LearningIRJET Journal
This document reviews methods for detecting COVID-19 using deep learning techniques applied to chest X-rays and CT scans. It summarizes several research papers that have used convolutional neural networks and techniques like transfer learning to analyze medical images and accurately classify patients as COVID-19 positive or normal. The research shows these deep learning models can detect COVID-19 from images with high accuracy, even outperforming traditional PCR tests. Larger datasets are still needed to improve the models. Overall, the document concludes medical image analysis with deep learning is a promising approach for fast and effective COVID-19 detection.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Enhancing Pneumonia Detection: A Comparative Study of CNN, DenseNet201, and V...IRJET Journal
This document presents a comparative study evaluating the performance of three deep learning models - a custom CNN, DenseNet201, and VGG16 - in classifying chest X-ray images to detect pneumonia. The CNN model achieved the best performance with 80% accuracy, comparable to human radiologists. A chest X-ray dataset was used to train and evaluate the models. VGG16 consistently outperformed the other models, though all models showed potential for improving pneumonia diagnosis through rapid and accurate analysis of medical images using deep learning techniques.
This document discusses detecting pneumonia in chest X-rays using deep learning techniques. It begins by stating that pneumonia is a major cause of death worldwide, especially in children. The objective is to develop a deep learning framework to automatically diagnose pneumonia from chest X-rays to reduce human error. Various deep learning models like CNN, VGG-16 and MobileNetV2 are implemented and compared on a public dataset. VGG-16 achieved the highest accuracy of 94.3% among the models for detecting pneumonia. The document concludes that pneumonia can be identified and classified using deep learning models with VGG-16 performing best.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
The document describes a deep learning framework for joint segmentation and classification of lung nodules in CT images. It uses a VGG19 network-assisted VGG-SegNet model for segmentation, extracting deep features from VGG19, and combining with handcrafted features for classification. The methodology involves collecting CT images, segmenting nodules using VGG-SegNet, extracting deep and handcrafted features, concatenating the features, and classifying images using various classifiers. Experimental results on LIDC-IDRI and Lung-PET-CT-Dx datasets show the proposed approach achieves 97.83% accuracy using an SVM-RBF classifier.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Detection of chest pathologies using autocorrelation functionsIJECEIAES
An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
Deep Learning for Pneumonia Diagnosis: A Comprehensive Analysis of CNN and Tr...IRJET Journal
This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve patient outcomes.
Pneumonia prediction on chest x-ray images using deep learning approachIAESIJAI
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients.
Using Deep Learning and Transfer Learning for Pneumonia DetectionIRJET Journal
This document presents research on using deep learning and transfer learning models to detect pneumonia from chest x-ray images. The researchers collected a dataset of over 5,000 chest x-rays and split it into training and test sets. Data augmentation techniques were used to expand the training dataset size. Several deep learning models were trained on the data including CNN, DenseNet121, VGG16, ResNet50, and Inception v3. Transfer learning was also utilized by training models pre-trained on ImageNet. The Inception v3 model achieved the highest testing accuracy of 80.29% for pneumonia detection. The researchers concluded the deep learning models could help radiologists diagnose pneumonia but that more work is needed to localize affected lung regions.
One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Selfattention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are nonCOVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where nonCOVID-19 cases could cover more than just a normal condition.
Employing deep learning for lung sounds classificationIJECEIAES
This document summarizes research on classifying lung sounds using deep learning models. It presents two models: 1) A convolutional neural network (CNN) model developed from scratch to classify lung sound spectrogram images into six classes with an accuracy of 91%. 2) A transfer learning approach using the pre-trained AlexNet network on the same dataset, which achieved a higher accuracy of 94%. A comparison to prior research achieving 80% accuracy shows that the transfer learning approach was more effective for lung sound classification. The document concludes that transfer learning is an effective method for classification when datasets are small.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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This document presents a convolutional neural network model to detect pneumonia from chest x-ray images. The model is trained from scratch on a dataset of over 5,800 chest x-ray images categorized into pneumonia and normal images. The model uses preprocessing like resizing and normalization, data augmentation, and a custom sequential CNN model with convolutional and pooling layers to extract features and classify images. Evaluation metrics like precision, recall, accuracy and F1 score are used to analyze the trained model's performance at detecting pneumonia from chest x-rays. The proposed system aims to help diagnose pneumonia early and assist medical professionals, especially in remote areas.
A Review Paper on Covid-19 Detection using Deep LearningIRJET Journal
This document reviews methods for detecting COVID-19 using deep learning techniques applied to chest X-rays and CT scans. It summarizes several research papers that have used convolutional neural networks and techniques like transfer learning to analyze medical images and accurately classify patients as COVID-19 positive or normal. The research shows these deep learning models can detect COVID-19 from images with high accuracy, even outperforming traditional PCR tests. Larger datasets are still needed to improve the models. Overall, the document concludes medical image analysis with deep learning is a promising approach for fast and effective COVID-19 detection.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Enhancing Pneumonia Detection: A Comparative Study of CNN, DenseNet201, and V...IRJET Journal
This document presents a comparative study evaluating the performance of three deep learning models - a custom CNN, DenseNet201, and VGG16 - in classifying chest X-ray images to detect pneumonia. The CNN model achieved the best performance with 80% accuracy, comparable to human radiologists. A chest X-ray dataset was used to train and evaluate the models. VGG16 consistently outperformed the other models, though all models showed potential for improving pneumonia diagnosis through rapid and accurate analysis of medical images using deep learning techniques.
This document discusses detecting pneumonia in chest X-rays using deep learning techniques. It begins by stating that pneumonia is a major cause of death worldwide, especially in children. The objective is to develop a deep learning framework to automatically diagnose pneumonia from chest X-rays to reduce human error. Various deep learning models like CNN, VGG-16 and MobileNetV2 are implemented and compared on a public dataset. VGG-16 achieved the highest accuracy of 94.3% among the models for detecting pneumonia. The document concludes that pneumonia can be identified and classified using deep learning models with VGG-16 performing best.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
The document describes a deep learning framework for joint segmentation and classification of lung nodules in CT images. It uses a VGG19 network-assisted VGG-SegNet model for segmentation, extracting deep features from VGG19, and combining with handcrafted features for classification. The methodology involves collecting CT images, segmenting nodules using VGG-SegNet, extracting deep and handcrafted features, concatenating the features, and classifying images using various classifiers. Experimental results on LIDC-IDRI and Lung-PET-CT-Dx datasets show the proposed approach achieves 97.83% accuracy using an SVM-RBF classifier.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Detection of chest pathologies using autocorrelation functionsIJECEIAES
An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
Deep Learning Approaches for Diagnosis and Treatment of COVID-19IRJET Journal
The document discusses using deep learning approaches for diagnosing and treating COVID-19. It first provides background on deep learning and convolutional neural networks. It then discusses challenges in early COVID-19 diagnosis and the need for computer-assisted diagnosis methods. The paper reviews several existing studies that used deep learning on CT scans and X-rays to classify COVID-19. It proposes developing a COVID-19 diagnosis system using a lung CT image dataset and deep learning models. The system would be designed, implemented and tested to efficiently detect COVID-19 infections from CT scans.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
MARIE: VALIDATION OF THE ARTIFICIAL INTELLIGENCE MODEL FOR COVID-19 DETECTIONijma
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.
An intelligent approach for detection of covid by analysing Xray and CT scan ...IRJET Journal
This document presents an intelligent approach to detect COVID-19 using X-ray and CT scan images. It discusses developing a deep learning model using a convolutional neural network (CNN) to analyze medical images and classify them as coming from COVID-19 positive or negative cases. The model would be integrated into a web application using Flask that allows users to upload images for rapid diagnosis. The goal is to address issues with traditional testing methods that can take days to get results, and to help reduce the spread of COVID-19 through faster detection. The document reviews several related studies applying deep learning to COVID-19 detection from medical images and discusses the materials and methodology used to develop and evaluate the proposed intelligent detection system.
Deep Learning for Pneumonia Diagnosis: A Comprehensive Analysis of CNN and Tr...IRJET Journal
This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve patient outcomes.
Pneumonia prediction on chest x-ray images using deep learning approachIAESIJAI
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients.
Using Deep Learning and Transfer Learning for Pneumonia DetectionIRJET Journal
This document presents research on using deep learning and transfer learning models to detect pneumonia from chest x-ray images. The researchers collected a dataset of over 5,000 chest x-rays and split it into training and test sets. Data augmentation techniques were used to expand the training dataset size. Several deep learning models were trained on the data including CNN, DenseNet121, VGG16, ResNet50, and Inception v3. Transfer learning was also utilized by training models pre-trained on ImageNet. The Inception v3 model achieved the highest testing accuracy of 80.29% for pneumonia detection. The researchers concluded the deep learning models could help radiologists diagnose pneumonia but that more work is needed to localize affected lung regions.
One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Selfattention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are nonCOVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where nonCOVID-19 cases could cover more than just a normal condition.
Employing deep learning for lung sounds classificationIJECEIAES
This document summarizes research on classifying lung sounds using deep learning models. It presents two models: 1) A convolutional neural network (CNN) model developed from scratch to classify lung sound spectrogram images into six classes with an accuracy of 91%. 2) A transfer learning approach using the pre-trained AlexNet network on the same dataset, which achieved a higher accuracy of 94%. A comparison to prior research achieving 80% accuracy shows that the transfer learning approach was more effective for lung sound classification. The document concludes that transfer learning is an effective method for classification when datasets are small.
Similar to Classification of COVID-19 from CT chest images using convolutional wavelet neural network (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
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Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
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- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
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- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
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- Create role with administrative privileges.
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- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Classification of COVID-19 from CT chest images using convolutional wavelet neural network
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 1078~1085
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp1078-1085 1078
Journal homepage: http://ijece.iaescore.com
Classification of COVID-19 from CT chest images using
convolutional wavelet neural network
Dina A. AbdulQader1
, Asma T. Saadoon1
, Marwa T. Naser1
, Ali Hassan Jabbar2
1
Department of Computer Engineering, University of Baghdad, Baghdad, Iraq
2
Ministry of Health, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received May 7, 2022
Revised Jul 31, 2022
Accepted Sep 17, 2022
Analyzing X-rays and computed tomography-scan (CT scan) images using a
convolutional neural network (CNN) method is a very interesting subject,
especially after coronavirus disease 2019 (COVID-19) pandemic. In this
paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya
(Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to
diagnose if they have COVID or not using CNN. The total data being tested
has 15000 CT-scan images chosen in a specific way to give a correct
diagnosis. The activation function used in this research is the wavelet
function, which differs from CNN activation functions. The convolutional
wavelet neural network (CWNN) model proposed in this paper is compared
with regular convolutional neural network that uses other activation
functions (exponential linear unit (ELU), rectified linear unit (ReLU),
Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave
better results for all performance metrics (accuracy, sensitivity, specificity,
precision, and F1-score). The results obtained show that the prediction
accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using
wavelet filters (rational function with quadratic poles (RASP1), (RASP2),
and polynomials windowed (POLYWOG1), superposed logistic function
(SLOG1)) as activation function, respectively. Using this algorithm can
reduce the time required for the radiologist to detect whether a patient has
COVID or not with very high accuracy.
Keywords:
Activation function
Artificial intelligence
Classification
Convolutional neural network
Coronavirus disease 2019
Deep learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Dina A. AbdulQader
Department of Computer Engineering, University of Baghdad
Baghdad, Iraq
Email: dina_aldaloo@coeng.uobaghdad.edu.iq
1. INTRODUCTION
Globally, in April 5, 2022, there had been 489,060,735 confirmed cases of coronavirus disease 2019
(COVID-19), including 6,150,333 deaths, reported to the World Health Organization [1]. COVID-19
represents the greatest global public health emergency since the pandemic of Spanish influenza in 1918 [2].
Earlier COVID-19 diagnosis plays a crucial role in not just patient management but also the prevention of the
further spread of the disease [3], [4]. COVID-19 spreads rapidly and needs early prediction and diagnosis to
decrease deaths caused by it [5], [6]. Several recent studies mention that artificial intelligence (AI) and
machine learning can be used to deal with COVID-19 [7]–[12], while the others suggested that using
convolutional neural network (CNN) to detect and classify COVID-19 based on computerized tomography
(CT) scan images [13]–[18].
Chest CT imaging may be utilized to classify COVID-19 infected individuals early [19]. Diagnosing
COVID-19 using a CT scan is better and more accurate than other methods [20], [21]. CNN is widely used in
2. Int J Elec & Comp Eng ISSN: 2088-8708
Classification of COVID-19 from CT chest images using convolutional wavelet … (Dina A. AbdulQader)
1079
medicine and is especially effective in the present COVID-19 epidemic [22], where X-ray and CT images of
COVID-19 patients are used to diagnose COVID-19 with the application of CNNs. A light CNN design had
been proposed based on the model of the SqueezeNet [23]. Reshi et al. [24] used a chest X-ray image for the
diagnosis of COVID-19 using deep CNN architecture. Szandała [25] says, “The primary neural networks
decision-making units are activation functions. Moreover, they evaluate the output of networks neural node;
thus, they are essential for the performance of the whole network. Hence, it is critical to choose the most
appropriate activation function in neural networks calculation.” In [26], all of the CT-scan images were
sorted in a way to exclude the unnecessary CT-scan images in order to reach the desired solution in a more
efficient way, so in this paper, this method is also followed. In this paper, a simple CNN is proposed with
wavelet function as an activation function to classify COVID-19 cases based on CT-SCAN images. Using
this algorithm can help the radiologist examine and report a huge number of daily cases with much less time
than if done manually (regularly, it takes ten minutes to examine and report a single case).
2. THE PROPOSED CONVOLUTIONAL WAVELET NEURAL NETWORK
The architecture of the CWNN used in this work is simple and is composed of only 12 layers, as
shown in Figure 1. Increasing CNN complexity and length (more than 12 layers) is time-consuming and may
lead to overfitting. However, simple CNN may not perform as accurately as complex CNNs in complicated
problems such as COVID-19 classification.
In addition, the wavelet function has many features that give the ability to find out important details
on a small scale. In fact, wavelet is successful to assess on both small and large scale together. So, in this
work a convolutional wavelet neural network (CWNN) structure is proposed that is a combination of a
simple CNN and wavelet function as the activation function to obtain an easy and accurate system that can
deal with complicated problems.
Figure 1. The proposed CWNN
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Int J Elec & Comp Eng, Vol. 13, No. 1, February 2023: 1078-1085
1080
From Figure 1, the proposed CWNN includes twelve layers. The first layer consists of an input
image convolved with 32 filters of size 3×3. The second layer consists of an activation layer, which will be
one of the wavelet functions. The third layer is a max pooling layer with filters of size 2×2. The fourth layer
consists of convolution operation with 64 filters of size 3×3. The fifth layer consists of an activation layer,
which will be one of the wavelet functions. The sixth layer is a max pooling layer with filters of size 2×2.
The seventh layer consists of convolution operation with 128 filters of size 3×3. The eighth layer consists of
an activation layer, which will be one of the wavelet functions. The ninth layer is a max pooling layer with
filters of size 2x2 followed by a fully connected layer with 128 units and activation layer which will be one
of the wavelet functions, respectively. The last layer is a SoftMax output layer with 2 possible classes. As an
activation function, several wavelet functions were proposed and used for example rational function with
quadratic poles (RASP1), (RASP2), polynomials windowed (POLYWOG1), and superposed logistic function
(SLOG1). The model was compiled with stochastic gradient descent (SGD) optimizer with 0.001 learning
rate and 0.9 momentum. Categorical cross entropy was used.
2.1. Testing the proposed system
In order to test the proposed CWNN algorithm, a dataset is required. The dataset used is composed
of CT-scan images with COVID and non-COVID cases. These CT-scan images were collected from Iraqi
hospital with the help of a specialist. These images were taken from 423 patients where all of the private
information was excluded.
The CT-scan images for each patient include at least 50 images, like a movie that show different
positions of the patient’s lungs as shown in Figure 2. For each patient, these images were sorted very
carefully in order to exclude images that do not give a good insight into the patient’s lungs and only images
that have the lungs open and clear were chosen (lung dependent areas). The chosen images are good for
examination and lead CWNN to accurate results instead of going through images that are non-beneficial. The
total data collected was originally 23,472 and after sorting them they became 15,000 images.
Figure 2. Images of a patient’s lungs (chest), the images in the rectangles are excluded
Then these images have been labeled as COVID and non-COVID. The COVID folder includes
CT-scan images of patients diagnosed with positive COVID, whereas the non-COVID folder contains
CT-scan images of patients diagnosed with negative COVID. Figure 3 depicts some samples of each class.
Figure 3(a) depicts examples of CT scan images from patients with positive COVID. Figure 3(b) depicts
examples of CT scan images from patients with negative COVID.
All information about the dataset is shown in Figures 4 and 5 in detail, which illustrate the age
groups and genders of different patients, respectively. Figure 4 shows that the data samples are chosen for
both males and females. This indicates that the algorithm can work on both genders. Figure 5 shows that the
data samples take different age groups of the population, indicating that the algorithm can perform on all the
population and not just on a specific age group like adults only.
Table 1 shows a detailed description of the dataset where the non-COVID patients’ number is 284,
the number of CT scan images for these patients is 15680, and the chosen images are 10,000. Among these
patients are 138 females and 146 males. While the COVID patients’ number is 139, and the number of CT
scan images for these patients are 7,792 and the chosen images are 5,000. Of these patients, 58 are females
and 81 are males. So, the total patients’ number is 423 and the total number of CT scan images for these
4. Int J Elec & Comp Eng ISSN: 2088-8708
Classification of COVID-19 from CT chest images using convolutional wavelet … (Dina A. AbdulQader)
1081
patients is 23,472, and the total chosen images are 15,000. The females’ total number are 196 and the males’
total number are 227.
(a) (b)
Figure 3. Samples of the data (a) COVID-19 CT scan images (b) non COVID CT scan images
Figure 4. CT-scan count based on gender
Figure 5. CT-scan count based on age
Table 1. Dataset information
Patients No. of patient’s images No. of used images Female Male
COVID 139 7,792 5,000 58 81
Non COVID 284 15,680 10,000 138 146
Total 423 23,472 15,000 196 227
2.2. Preprocessing
In the preprocessing stage, all the images chosen in the dataset are resized to 224×244 pixels and
also normalized. The normalized data is now divided: 70% of the data is used for the training stage, 10% of
the data is used for the validation stage and 20% of the data is used for the testing stage. Because the dataset
images include more than one image for the same patient, these images are either in training, validation, or
testing to avoid overlapping.
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1082
2.3. Evaluation metrics
Four metrics are used to evaluate the performance of the proposed model on the test dataset, these
metrics are: i) true positive (Tp) for the COVID images has been classified (predicted) correct as COVID; ii)
false negative (Fn) for the COVID images has been classified incorrectly as non-COVID; iii) true negative
(Tn) for the non-COVID images has been classified correctly as non-COVID; iv) false positive (Fp) for the
non-COVID images has been classified incorrectly as COVID. These metrics are described by the confusion
matrix [22] shown in Figure 6. Accuracy, precision, sensitivity, specificity, and F1-score [26], [27] are
calculated using (1)-(5).
Accuracy =
Tp+Tn
Tp+Tn+Fp+Fn
(1)
Precision =
Tp
Tp+Fp
(2)
Sensitivity =
𝑇𝑝
𝑇𝑝+𝐹𝑛
(3)
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
𝑇𝑛
𝑇𝑛+𝐹𝑝
(4)
𝐹1 − 𝑠𝑐𝑜𝑟𝑒 = 2 ∗
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛∗sensitivity
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+sensitivity
(5)
Figure 6. Confusion matrix
2.4. Experimental setup
Python language 3.9.7 is used in this paper. Also, TensorFlow 2.7.0 framework is used in this
project. This was accomplished using an Intel Core i5 Lenovo computer running Windows 10 Pro (64-bit
operating system).
3. RESULTS AND DISCUSSION
The proposed CWNN’s efficiency is tested by calculating the four metrics (Tp, Fn, Tn, and Fp). For
comparison with other previous models, the same dataset is trained and tested with the same model
architecture but with other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU),
Swish, LeakyReLU, Sigmoid). The calculated four metrics values are shown using the confusion matrices as
in Figure 7. Figure 7(a) shows that when RASP1 is used as the activation function, only 1 COVID-19 out of
995 is predicted as non-COVID. As shown in Figure 7(b), using POLYWOG1 as the activation function, all
COVID-19 out of 995 are predicted as COVID. Figure 7(c) shows that when RASP2 is used as the activation
function, only 1 COVID-19 out of 995 is predicted as non-COVID. Figure 7(d) shows that, when SLOG1 is
used as the activation function, only 7 COVID-19 out of 995 are predicted as non-COVID. On the other
hand, when ELU is used as the activation function, 41 COVID-19 out of 995 is predicted as non-COVID, as
illustrated in Figure 7(e). Using ReLU as the activation function, 65 COVID-19 out of 995 is predicted as
non-COVID as shown in Figure 7(f). Using Swish as the activation function, 53 COVID-19 out of 995 is
predicted as non-COVID as illustrated in Figure 7(g). Using LeakyReLU as the activation function,
6. Int J Elec & Comp Eng ISSN: 2088-8708
Classification of COVID-19 from CT chest images using convolutional wavelet … (Dina A. AbdulQader)
1083
79 COVID-19 out of 995 is predicted as non-COVID as illustrated in Figure 7(h). Figure 7(i) shows that
when the Sigmoid is used as the activation function, 248 COVID-19 out of 995 is predicted as non-COVID.
The efficiency metrics are calculated again to compare them with the proposed CWNN as shown in
Table 2. Overall, CWNN gave better accuracy, precision, sensitivity, specificity, and F1-score compared to
others. From Table 2, an evaluation is made in which the accuracy of the proposed CWNN is a lot higher
than different approaches, in which, as an example, the accuracy of the proposed CWNN with RASP1
activation function is 0.9997, whereas the accuracy when employing CNN with Swish activation function is
0.9252. Thus, the preceding instance shows that the use of CWNN is better than other CNNs.
From the confusion matrices in Figure 7 and all the numbers in Table 2, it is obvious that CWNN
with a wavelet activation function achieves better prediction than CNN with other activation functions. All
the findings obtained from applying the algorithm of the proposed CWNN were as expected, that is, higher
than regular CNN due to using the wavelet activation function. These outcomes are obtained because of the
features of the wavelet function that give a higher overall performance for the system.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 7. Confusion matrix, CWNN: (a) RASP1, (b) POLYWOG1, (c) RASP2, and (d) SLOG1);
and regular CNN: (e) ELU, (f) ReLU, (g) Swish, (h) Leaky ReLU, and (i) Sigmoid
7. ISSN: 2088-8708
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Table 2. Efficiency results
Accuracy Precision Sensitivity Specificity F1-score
Rasp1 0.9997 1 0.998995 1 0.999497
Polywog1 0.9997 0.998996 1 0.999503 0.999498
Rasp2 0.999 0.997992 0.998995 0.999006 0.998493
Slog1 0.9904 0.978218 0.992965 0.989071 0.985536
ELU 0.9794 0.978462 0.958794 0.989568 0.968528
ReLU 0.9624 0.95092 0.934673 0.976155 0.942727
Swish 0.9252 0.845601 0.946733 0.914555 0.893314
LeakyReLU 0.9418 0.905138 0.920603 0.95231 0.912805
Sigmoid 0.8584 0.807568 0.750754 0.911575 0.778125
4. CONCLUSION
In this paper, a simple CNN with a wavelet function (RASP1, POLYWOG1, RASP2, SLOG1) is
used for predicting the COVID-19 disease and a prediction accuracy of 99.97%, 99.97%, 99.9%, and 99.04%
respectively is achieved. The same CNN structure is used with other commonly used activation functions
(ELU, ReLU, Swish, LeakyReLU, Sigmoid), and the results using the CWNN achieve better prediction and
accuracy results. Using this algorithm is very helpful and precise in detecting COVID cases with high speed,
therefore reducing the time required by the radiologist to send a report and especially in hospitals with a large
number of patients. In the future, it is planned to use the proposed model in automated prediction systems and
to predict the cases’ severity and their prognosis (future outcome). It is also planned to test CWNN for other
predicted problems.
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BIOGRAPHIES OF AUTHORS
Dina A. AbdulQader received the B.Sc. degree in computer engineering from
Baghdad University, Iraq, in 2006 and the M.Sc. degree in Computer Engineering
from the University of Baghdad, Iraq, in 2012. Since 2007, she has been a faculty member
with the College of Engineering at the University of Baghdad, Iraq. Her research interests
include machine learning, neural network, artificial intelligence, convolutional neural network,
image processing, and signal processing. She can be contacted at email:
dina_aldaloo@coeng.uobaghdad.edu.iq.
Asma T. Saadoon received the B.Sc. degree in Electrical Engineering from
Baghdad University, Iraq, in 2004 and the M.Sc. degree in Electronics & Communication
Engineering from the University of Baghdad, Iraq, in 2007. Since 2007, she has been
a faculty member with the University of Baghdad. Her research interests include robotics,
electronic engineering, and embedded systems. She can be contacted at email:
asmatahaeeng@coeng.uobaghdad.edu.iq.
Marwa T. Naser received an M.Sc. degree in Computer Engineering from the
University of Baghdad, Iraq in 2016. She currently is an assistant lecturer and is working as a
faculty member in the College of Engineering at the University of Baghdad, Iraq. She has
published two research articles. Her research interests include computer network, transport
protocols, routing protocols, MANET, FANETS, wireless network, WSN, IoT, network
simulation NS2 and NS3, and neural network. She can be contacted via email at
marwa_taher84@coeng.uobaghdad.edu.iq.
Ali Hassan Jabbar received the B.Sc. degree from Collage of Medicine Al-
Mustansiriya University, Baghdad, Iraq, in 1997. He is a fellow of Iraqi Council of Medical
Specialization Aesthetic and Intense Care in 2011. He is currently Anesthesia Specialist,
Madenat Al Emammain Al Kadhmain Medical City, Baghdad, Iraq. He is a member of Iraqi
society for aesthesia intensive care and pain medicine. He is a member of Iraqi society for pain
management. He can be contacted at ali.h.jabbar369@gmail.com.