DETECTION OF COVID-19 IN LOW ENERGY CHEST X-RAYS USING FAST R-CNN

Maryam Kareem Sakran MAMOORİ; Abdullahi Abdu IBRAHİM

Makale detayları

DETECTION OF COVID-19 IN LOW ENERGY CHEST X-RAYS USING FAST R-CNN

Yazarlar:

Maryam Kareem Sakran MAMOORİ | Abdullahi Abdu IBRAHİM

Yayıncı:

Altınbaş University

Ana Başlıklar:

Kimya mühendisliği

Dergi:

Aurum Journal of Health Sciences 2022 Vol.4 Issue 1, pp.34-43

Belge Tipi:

Makale

Yayın Tarihi:

4/2022

Belge Dili:

Türkçe

Document ID (Org No.):

2000692358

Sıklık:

Üç Yıllık

pISSN:

2651-2815

Özet:

 

In recent years, it has been shown that deep learning can produce similar performance increases in the domain of medical image analysis for object detection and segmentation tasks. Notable recent work includes important medical applications, for example, in the field of pulmonology (classification of lung diseases and detection of pulmonary nodules on CT images in this paper, we present a variation of CNNs, which works extremely well on a current data set — a customized architecture with optimal parameters. In our contribution, we focus on lowering the complexity of our network, while yet reaching a phenomenally high degree of accuracy. To achieve this aim, our model has been tailored for high performance and an easy design.

Makale detayları

DETECTION OF COVID-19 IN LOW ENERGY CHEST X-RAYS USING FAST R-CNN

Yazarlar: Maryam Kareem Sakran MAMOORİ | Abdullahi Abdu IBRAHİM

Yayıncı: Altınbaş University

Ana Başlıklar: Kimya mühendisliği

Dergi: Aurum Journal of Health Sciences 2022 Vol.4 Issue 1, pp.34-43

Belge Tipi: Makale

Yayın Tarihi: 4/2022

Belge Dili: Türkçe

Document ID (Org No.): 2000692358

Sıklık: Üç Yıllık

pISSN: 2651-2815

Sayı

Yazarlar:

Maryam Kareem Sakran MAMOORİ | Abdullahi Abdu IBRAHİM

Yayıncı:

Altınbaş University

Ana Başlıklar:

Kimya mühendisliği

Dergi:

Aurum Journal of Health Sciences 2022 Vol.4 Issue 1, pp.34-43

Belge Tipi:

Makale

Yayın Tarihi:

4/2022

Belge Dili:

Türkçe

Document ID (Org No.):

2000692358

Sıklık:

Üç Yıllık

pISSN:

2651-2815