Histogram Features Extraction for Edge Detection Approach

Najjar, Fallah H.; Khudhair, Kifah T.; Khaleq, Ali Hussein Abdul; Kadhim, Ola N.; Abedi, Firas; Al‐Kharsan, Ibrahim H.

doi:10.1109/iiceta54559.2022.9888697

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The COVID-19 class contains 822 images, while the NORMAL class contains 1577 images [24][25][26][27]. However, we used the proposed method to evaluate ACC, MCC, SEN, F1, PRE, and SP metrics (equations [25][26][27][28][29][30][31][32].…”

Section: Resultsmentioning

confidence: 99%

“…In this experiment, we calculate the evaluation metrics of the SVM and the KNN classifiers for the GLCM1, GLCM2, GLCM3, GLCM4, and GLCMA datasets. Furthermore, this subsection compares the proposed method with state-of-the-art algorithms [28][29][30][31][32][33]. Furthermore, the experimental results of the proposed method are collected on the COVID GLCM dataset using SVM and KNN classifiers.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

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.

View full text Add to dashboard Cite

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“…The investigation was implemented for four distinct samples of pictures and four distinct secrets. The visual assessment of the histogram is critical in evaluating the findings-standard assessment of the picture and standard evaluation utilizing standard measurements [16,17]. The results were obtained by experimenting with the four pictures of varying sizes of type (.bmp), as indicated in Figure 2.…”

Section: Visualization and Analysismentioning

confidence: 99%

Random Color Image Encryption Using the Genetic Algorithm

Salman,

Mahdi

2023

Mal. J. Fund. Appl. Sci.

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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.

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