Covid-19 X-ray image classification using SVM based on Local Binary Pattern

Al-jumaili, Saif; Al-Azzawi, Athar; Duru, Adil Deniz; İbrahim, Abdullahi Abdu

doi:10.1109/ismsit52890.2021.9604731

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…The symptoms of Covid, it was very similar to the flu, the most common symptoms of COVID-19 are fever, headache, sore throat, cough, severe pneumonia, septic shock, runny nose, fatigue, muscle pain, diarrhea, hemoptysis, dyspnea, lymphatic distress, and distress syndrome. acute respiratory [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Transfer Learning Methodologies on the COVID-19 Radiographic Chest Images

Al-Azzawi¹,

Al-jumaili²,

Duru³

et al. 2023

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In 2019, the world had been attacked with a severe situation by the new version of the SARS-COV-2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1score, and Matthew's correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID-19.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Transfer Learning Methodologies on the COVID-19 Radiographic Chest Images

Al-Azzawi¹,

Al-jumaili²,

Duru³

et al. 2023

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show abstract

“…From that, science has developed to include the medical field, in particular through radiographic images, that may help the pathology in the health sector. The use of modern technologies such as Deep Learning is a great deal in the field of analysis and diagnosis since it has proven high efficiency and great accuracy in analyzing several types of radiographic diseases such as Covid19 [2,3,4], Alzheimer's [5,6], Brain tumors [7]... etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Methods for Classification Colorectal Cancer Based on Histology Images

Alhanaf

Al-jumaili

Bilgin

et al. 2022

2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.

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“…Many types of deep learning models provide aid for healthcare sector due to the ability to classify the images with high accuracy and reduce time-consumption. Particularly in the medical sector, it is used with various imaging modalities such as Histology [8], Computer Tomography (CT) [9], Microscopy [10,11], positron emission tomography (PET), X-Ray [12], Ultrasound [13], Singlephoton emission computed tomography (SPECT), and Magnetic resonance imaging (MRI) [14,15]. The primary goal of medical images is to effectively diagnose diseases by clinicians and radiologists.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Convolutional Architectures in Medical Applications: Classical or Quantum?

Al-jumaili

Al-Jumaili²,

Alyassri

et al. 2022

2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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Deep learning is one of the most significant advances in AI (AI). It is used in a variety of fields due to it has the ability to solve problems that cannot be handled by traditional technologies. The optimization of deep learning relevant to medical images is one of the most important recent advances in image analysis. Several developments have been done on Convolutional Neural Networks to achieve optimal accuracy and increase the learning speed. However, in this paper, we discuss the most recent innovations in convolutional neural networks within Classical method and Quantum method. We briefly provide a snapshot about the architecture, improvements, and principles of both (Classical and Quantum).

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Covid-19 X-ray image classification using SVM based on Local Binary Pattern

Cited by 8 publications

References 17 publications

Evaluation of Deep Transfer Learning Methodologies on the COVID-19 Radiographic Chest Images

Evaluation of Deep Transfer Learning Methodologies on the COVID-19 Radiographic Chest Images

Deep Transfer Learning Methods for Classification Colorectal Cancer Based on Histology Images

Recent Advances on Convolutional Architectures in Medical Applications: Classical or Quantum?

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