Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images
المؤلف | Ahishali M. |
المؤلف | Degerli A. |
المؤلف | Yamac M. |
المؤلف | Kiranyaz, Mustafa Serkan |
المؤلف | Chowdhury M.E.H. |
المؤلف | Hameed K. |
المؤلف | Hamid T. |
المؤلف | Mazhar R. |
المؤلف | Gabbouj M. |
تاريخ الإتاحة | 2022-04-26T12:31:19Z |
تاريخ النشر | 2021 |
اسم المنشور | IEEE Access |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/ACCESS.2021.3064927 |
الملخص | Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Convolutional neural networks Deep learning Diagnosis HTTP Medical imaging Benchmark datasets Chest X-ray image Data classification Early diagnosis Learning approach Machine learning techniques Medical doctors Software implementation Learning systems |
النوع | Article |
الصفحات | 41052-41065 |
رقم المجلد | 9 |
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