Soft Edge Detection by Mamdani Fuzzy Inference of Color Image

In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.

Section: Design Of the Proposed Methodsmentioning

confidence: 99%

Coronavirus Classification based on Enhanced X-ray Images and Deep Learning

Najjar,

Waheed,

Mahdi

2023

“…And so on for all locations and all angular; see Figure 5. In the last step, the twenty-two texture features of the GLCM matrices we used to perform the proposed method are defined in Table 1 [10,18,19]. Where 𝑄 = ∑…”

Section: 𝑝(𝑟 𝑐|𝑑 𝜃) = 𝑁 𝑑𝜃 (𝑟 𝑐) 𝑁mentioning

confidence: 99%

“…Typically, GLCM is known for its ability to extract robust statistical texture features, with many researchers utilizing it as a secondary feature extraction method, in addition to other techniques, in the MATLAB image processing toolbox, where only four features (contrast, correlation, energy, and inverse difference moment/homogeneity) [9][10][11][12][13] 2). However, GLCM is usually used as a secondary feature extractor method [14,15].…”

Section: Introductionmentioning

confidence: 99%

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

Najjar,

Kadhim,

Kareem

et al. 2023

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.

“…The proposed approach will employ a proportionately big secret key (Represented with a hexadecimal number system) in the form of a byte matrix of size (16 × 16). This key was used for replacement operations and transposition and provides propagation characteristics with good obfuscation in an encrypted form [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Random Color Image Encryption Using the Genetic Algorithm

Salman,

Mahdi

2023

This paper implements a combined pseudo-random sequence generator based on neural networks and chaotic, random variable color images based on an algorithm for chaotic encryption, secure image storage, and transmission. The generator that controls the operation of the encryption algorithm: the arrangement of pixel positions, the use of random color images of the AES algorithm to achieve encryption, creation algorithms, and neural networks by dynamically updating control parameters and numbers to increase the generated chaotic sequence randomness by using simulation MATLAB for getting output. The chaotic function's iteration of the neural networks. The experimental results are presented in the form of a pixel correlation coefficient and security analysis to prove the security and effectiveness of the proposed chaos-based ANN encryption.