SIAMESE NEURAL NETWORKS FOR PANDEMIC DETECTION USING CHEST RADIOGRAPHS
Yazarlar (6)
Vanita Jain
Apoorv Jain
Vinayak Gark
Achin Jain
Dr. Öğr. Üyesi Merve DEMİRCİ
Kafkas Üniversitesi, Türkiye
Müslüm Cengiz Taplamacıoğlu
Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal on “Technical and Physical Problems of Engineering” (IJTPE) | ||
| Dergi ISSN | 0051-1402 | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2022 |
| Cilt / Sayı / Sayfa | 14 / 2 / 104–110 | DOI | – |
| Makale Linki | http://www.iotpe.com/IJTPE/IJTPE-2022/IJTPE-Issue51-Vol14-No2-Jun2022/13-IJTPE-Issue51-Vol14-No2-Jun2022-pp104-110.pdf | ||
| Özet |
| The recent developments in the field of deep learning have enabled the efficient diagnosis of medical imaging for determining a broad set of diseases. To reduce the spread and impact of the pandemic (COVID virus), machine learning techniques can be used to diagnose and predict the disease using chest X-ray images. In this research, we present an approach using Siamese Convolutional Neural Network (SCNN) to classify chest x-ray images into four classes, namely pandemic, Severe-COVID, Pneumonia and Normal. We present a comparative study between the performance of our Siamese network and other pre-trained CNN architectures ie VGG-16 and ResNet50 in this research. The model performance is tested by merging two publicly available datasets: COVID-Chest-Xray dataset and Chest X-Ray Images (Pneumonia). We achieved an accuracy of 98% on Siamese ResNet50 which gives the best performance in contrast to 95% on VGG-16, 93% on ResNet50 and 96% on Siamese VGG-16. |
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