| Yazarlar (6) |
Vanita Jain
|
|
Apoorv Jain
|
|
Vinayak Gark
|
|
Achin Jain
|
Dr. Öğr. Üyesi Merve DEMİRCİ
Kafkas Üniversitesi, Türkiye |
|
Müslüm Cengiz Taplamacıoğlu
Gazi Üniversitesi, Türkiye |
| 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. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | 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 No | 14 |
| Sayı | 2 |
| Sayfalar | 104 / 110 |
| Makale Linki | http://www.iotpe.com/IJTPE/IJTPE-2022/IJTPE-Issue51-Vol14-No2-Jun2022/13-IJTPE-Issue51-Vol14-No2-Jun2022-pp104-110.pdf |
