A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Automated Detection of Covid-19 from X-ray Using SVM

Earlier discovery of COVID-19 through precise diagnosis, particularly in instances with no evident symptoms, may reduce the mortality rate of patients. Chest X-ray images are the primary diagnostic tool for this condition. Patients exhibiting COVID-19 symptoms are causing hospitals to become overcrowded, which is becoming a big concern. The contribution that machine learning has made to big data medical research has been very helpful, opening up new ways to diagnose diseases. This study has developed a machine vision method to identify COVID-19 using X-ray images. The preprocessing stage has been applied to resize images and enhance the quality of X-ray images. The Gray-level co-occurrence matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) are then used to extract features from the X-ray images, and these features are combined to develop the performance classification via training by Support Vector Machine (SVM). The testing phase evaluated the model's performance using generalized data. This developed feature combination utilizing the GLCM and GLRLM algorithms assured a satisfactory evaluation performance based on COVID-19 detection compared to the immediate, single feature with a testing accuracy of 96.65%, a specificity of 99.54%, and a sensitivity of 97.98%. © 2022 IEEE.

Masked Face Images Based Gender Classification using Hybrid Bat Algorithm Optimized Bagging

The face is one of the biometrics utilized to learn information from a person, such as gender. Gender classification study is expanding daily as a result of how important it is and how many other sectors, like forensics, security, business, and others, employ it. However, in order to protect themselves and stop the spread of Covid-19 during this epidemic, everyone must wear a face mask. Because many crucial facial features that help determine a person's gender are obscured by masks, using one creates an issue for the gender classification system. To obtain optimal performance outcomes, suitable hyperparameters are also required. As a result, the objective of this study is to develop a gender categorization system based on mask-covered faces utilizing a novel technique that combines several features in the Gray Level Co-occurrence Matrix (GLCM), which is then fed into the Bagging classifier.A Hybrid Bat Algorithm (HBA) is used to optimize the bagging hyperparameters. With 97% accuracy, precision, recall, and f1-score values, the suggested model is demonstrated to have greater performance than before the hyperparameters were tuned using HBA. © 2023 IEEE.

Supervised Machine Learning Strategy for detection of covid19 patients

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies. © 2023 IEEE.

Comparison of Texture Feature Extraction Method for COVID-19 Detection With Deep Learning

This paper describes research on texture feature extraction for COVID-19 detection. Fractal Dimension Texture Analysis (FDTA) and Gray Level Co-occurrence Matrix (GLCM) were used for feature extraction. A dense neural network is used for classification. Three classes were used for classification to classify Normal, COVID-19, and Other pneumonia. The data entered in the texture feature extraction is a chest x-ray (CXR) image that is grey scaled and resized into 400400 pixels. Performance analysis of the model uses a confusion matrix. The best performance feature extraction method for detecting COVID-19 is FDTA, with an accuracy testing of 62.5%. © 2022 IEEE.

Satellite Data Investigation for Change Estimation During COVID Era by Fusing Pixel and Object-Based Technique

In this investigation, an innovative combination of pixel-based change detection technique and object-based change detection technique is explored with the satellite images of Holy Masjid al-Haram, Saudi Arabia. The gray-level co-occurrence matrix (GLCM) method is used to quantify the texture of the remote sensing data through the texture classification approach on the satellite data in this work. GLCM produces results of the texture quantification in normalized form. Thus, applying a texture classification scheme on the satellite data is impressive to observe. Later maximum likelihood image classification approach is used for classification purposes. The classified information is categorized into four different classes. The kappa coefficient’s value and the overall accuracy for the pre- COVID classified study area are 0.6532 and 76.38%, respectively. During COVID, the classified study area presents the kappa coefficient and the overall accuracy of 0.7631 and 82.18%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 using Wavelet Transform, Machine Learning and Deep Learning Models

Since December 2019, the world is fighting against the newly found virus named COVID-19 whose symptoms are closer to pneumonia. Being highly contagious, it has spread all over the world, and hence the World Health Organization has declared this as a global pandemic. Some patients infected with this virus have severe symptoms which are fatal. Hence the early discovery of COVID-19 infected patients is necessary to avoid further community spread. The available tests such as RTPCR and Rapid Antigen Tests are not 100% accurate and do not give quick results either. Therefore, it is the need of the hour to explore identification methodologies that are quick, accurate, and easily scalable. This work intends to do so using different machine learning and deep learning models. First, the step involves feature extraction using Gray Level Co-occurrence Matrix (GLCM) and classification with LightGBM classifier which gives an accuracy of 92.78%. This is then further improved to 95.79% using wavelets. Further, the CNN architectures with max-pooling and DWT layers are compared and it's found that CNN architecture with max-pooling layer gives better accuracy of 95.72%. Thus, this work presents a comparative analysis of Machine Learning Algorithms and CNN architectures for better accuracy and time. © 2022 IEEE.

GLCM Based LGBM classification for COVID-19 Analysis using Chest X-ray Images

The novel corona virus (COVID-19) has turned out to be the biggest challenge of 21 century. Since, it is spreading at a very high pace all over the world, fast and accurate detection of this virus becomes a necessity. However, the human annotation of images is time-consuming;it is not a good strategy for dealing with big amounts of medical imaging data. This work looks at the experimental examination of features that are well-suited for examining X-ray pictures in COVID19. This investigation encompasses the series of steps, including data augmentation, pre-processing, feature extraction using GLCM followed by feature selection using PCA, and finally classification is performed by Light Gradient Boosting Machine. The proposed method was validated by comparing it to COVID-19 X-ray dataset, with an accuracy achieved is 92.40%. © 2022 IEEE.

Texture Analysis of Ultrasound Images to Differentiate Pneumonia and Covid-19

Lung ultrasound can potentially diagnose lung abnormalities such as pneumonia and covid-19, but it requires high experience. Covid-19, as a global pandemic, has similar common symptoms as pneumonia. The proper diagnosis of covid-19 and pneumonia necessitates clinicians' high expertise and skill to classify Covid-19 disease. This paper presents an approach to differentiate pneumonia and covid-19 based on texture analysis of ultrasound images. The proposed scheme is based on the Gray Level Co-occurrence Matrix (GLCM) features computing with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma transformation for image enhancement. The results of the feature extraction analysis for lung ultrasound images suggest that differentiating pneumonia and Covid-19 is possible based on image texture features. © 2021 IEEE.