Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients
المؤلفون المشاركون
Fan, Zeming
Jamil, Mudasir
Sadiq, Muhammad Tariq
Huang, Xiwei
Yu, Xiaojun
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-24
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease.
Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose.
However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications.
In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data.
Transfer learning builds accurate models in a timesaving way.
First, data augmentation was performed to help the network for memorization of image details.
Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting.
All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method.
The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations.
The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Fan, Zeming& Jamil, Mudasir& Sadiq, Muhammad Tariq& Huang, Xiwei& Yu, Xiaojun. 2020. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186643
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Fan, Zeming…[et al.]. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1186643
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Fan, Zeming& Jamil, Mudasir& Sadiq, Muhammad Tariq& Huang, Xiwei& Yu, Xiaojun. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186643
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186643
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