Modify convolutional neural network model for the diagnosis of multi-classes lung diseases covid-19 and pneumonia based on x-ray images
المؤلفون المشاركون
Karim, Umar Sidqi
al-Sulaifani, Ahmad Khurshid Muhammad
المصدر
العدد
المجلد 25، العدد 1 العلوم الصرفة و الهندسية (30 يونيو/حزيران 2022)، ص ص. 63-73، 11ص.
الناشر
تاريخ النشر
2022-06-30
دولة النشر
العراق
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
COVID-19 is a new virus able to infect both the upper and lower respiratory lobes of ling.
There is a daily increase in cases and deaths in the global epidemic.
A number of the test kits now in use are sluggish and in short supply; hence RT-PCR testing is the most appropriate option.
To avoid a potentially fatal outcome, early detection of COVID-19 is essential.
According to numerous studies, visual markers (abnormalities) on a patient’s Chest X-Ray imaging can be a valuable characteristic of a COVID-19 patient, which can be exploited to discover the virus.
In this research, Convolutional Neural Networks (CNNs) are being proposed to detect the Covid-19 disease based on X-rays images.
The suggested model is based on modified VGG16 architecture for deep feature extraction.
The fine-tuning approach with end-toend training is also 63tilized in the aforementioned deep CNN models.
The suggested model has been trained and evaluated on the dataset contains 7,245 X-ray images, comprising 1,420 Covid-19 cases, 4,167 bacterial cases of Pneumonia, and 1,658 normal cases.
The model is evaluated using metrics such as accuracy, precision, recall, and f1-score.
The proposed model enhanced the accuracy by using less trainable parameters (weights) than the Vgg16 model.
Thus, the time needed for training and testing will be less.
In addition, it achieved a multiclass micro-average of 97% precision, 97% recall, 97% f1-score, and 97% classification accuracy.
The findings obtained show that the proposed strategy outperforms several currently used methods.
This model appears to be convenient and forceful for multiclass classification.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Karim, Umar Sidqi& al-Sulaifani, Ahmad Khurshid Muhammad. 2022. Modify convolutional neural network model for the diagnosis of multi-classes lung diseases covid-19 and pneumonia based on x-ray images. Journal of Dohuk University،Vol. 25, no. 1 العلوم الصرفة و الهندسية, pp.63-73.
https://search.emarefa.net/detail/BIM-1595293
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Karim, Umar Sidqi& al-Sulaifani, Ahmad Khurshid Muhammad. Modify convolutional neural network model for the diagnosis of multi-classes lung diseases covid-19 and pneumonia based on x-ray images. Journal of Dohuk University Vol. 25, no. 1 Pure and Engineering Sciences (2022), pp.63-73.
https://search.emarefa.net/detail/BIM-1595293
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Karim, Umar Sidqi& al-Sulaifani, Ahmad Khurshid Muhammad. Modify convolutional neural network model for the diagnosis of multi-classes lung diseases covid-19 and pneumonia based on x-ray images. Journal of Dohuk University. 2022. Vol. 25, no. 1 العلوم الصرفة و الهندسية, pp.63-73.
https://search.emarefa.net/detail/BIM-1595293
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references: p. 72-73
رقم السجل
BIM-1595293
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر