Weighted Cross-Entropy for Unbalanced Data with Application on COVID X-ray images
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Since December 2019 the world is infected by COVID-19 or Coronavirus disease, which spreads very quickly, out of control. The high number of precautions for laboratory access, which need to be taken to contain the virus, together with the difficulties in running the gold standard test for COVID-19, result in a practical incapability to make early diagnosis. Recent advances in deep learning algorithms allow efficient implementation of computer-aided diagnosis. This paper investigates on the performance of a very well known residual network, ResNet50, and a lightweight Atrous CNN (ACNN) network using a Weighted Cross-entropy (WCE) loss function, to alleviate imbalance on COVID datasets. As a result, ResNet50 model initialized with pre-trained weights fine-tuned by ImageNet dataset and exploiting WCE achieved the state-of-the-art performance on COVIDXRay-5K test set, with a top balanced accuracy of 99.87%. © 2020 IEEE.
Açıklama
2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 -- 15 October 2020 through 17 October 2020 -- -- 165305
Anahtar Kelimeler
Automatic Diagnosis, Classification, Coronavirus, Covıd-19, Deep Learning, Loss Functions, Weighted Cross-Entropy, Computer Aided İnstruction, Deep Learning, Disease Control, Entropy, Intelligent Systems, Learning Algorithms, Statistical Tests, Cross Entropy, Early Diagnosis, Efficient İmplementation, Gold Standards, Loss Functions, Out-Of-Control, State-Of-The-Art Performance, Unbalanced Data, Computer Aided Diagnosis
Kaynak
Proceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
WoS Q Değeri
Scopus Q Değeri
N/A
