GLCMs Based multi-inputs 1D CNN Deep Learning Neural Network for COVID-19 Texture Feature Extraction and Classification

Abbood, Elaf Ali; Al-Assadi, Tawfiq A.

doi:10.33640/2405-609x.3201

Cited by 6 publications

(2 citation statements)

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“…Studies in this field have produced a wealth of information on the topic, including GLCM feature extraction. In [16], a method was proposed that used GLCM to extract features from the image dataset with three distances and directions. However, this was then classified with the Dense fully connected and SoftMax layer.…”

Section: Related Workmentioning

confidence: 99%

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Najjar,

Kadhim,

Kareem

et al. 2023

Mal. J. Fund. Appl. Sci.

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As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.

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Section: Related Workmentioning

confidence: 99%

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Najjar,

Kadhim,

Kareem

et al. 2023

Mal. J. Fund. Appl. Sci.

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show abstract

“…For example, Oktay et al [26] reported a cardiac image approach that creates a high-resolution image through inputting several low-resolution images, which is helpful in medical treatment. Abbood and Al-Assadi [27] revealed that CT scans and X-Ray COVID-19 images have been used for multi-input CNN learning to diagnose COVID-19, increasing the accuracy to 98%. Elmoufidi et al [28] found that the accuracy of glaucoma assessment can be improved to 99.1% using fundus images in a multiple input system that employs VGG19, a CNN architecture.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Han

2024

Applied Sciences

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Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system.

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Detection Covid-19 Infection of Lung CT Scan Slices Images Based on a Transfer Learning and GRAD-CAM

Jafar

Ali

2022

2022 International Conference on Data Science and Intelligent Computing (ICDSIC)

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GLCMs Based multi-inputs 1D CNN Deep Learning Neural Network for COVID-19 Texture Feature Extraction and Classification

Cited by 6 publications

References 0 publications

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Detection Covid-19 Infection of Lung CT Scan Slices Images Based on a Transfer Learning and GRAD-CAM

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