DeepChestNet: Artificial intelligence approach for COVID-19 detection on computed tomography images
International Journal of Imaging Systems and Technology, cilt.33, sa.3, ss.776-788, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 33 Sayı: 3
- Basım Tarihi: 2023
- Doi Numarası: 10.1002/ima.22876
- Dergi Adı: International Journal of Imaging Systems and Technology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, INSPEC
- Sayfa Sayıları: ss.776-788
- Anahtar Kelimeler: artificial intelligence, computer-aided diagnosis system, COVID-19 detection, lung segmentation, pulmonary lobe segmentation
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
The conventional approach for identifying ground glass opacities (GGO) in medical imaging is to use a convolutional neural network (CNN), a subset of artificial intelligence, which provides promising performance in COVID-19 detection. However, CNN is still limited in capturing structured relationships of GGO as the texture and shape of the GGO can be confused with other structures in the image. In this paper, a novel framework called DeepChestNet is proposed that leverages structured relationships by jointly performing segmentation and classification on the lung, pulmonary lobe, and GGO, leading to enhanced detection of COVID-19 with findings. The performance of DeepChestNet in terms of dice similarity coefficient is 99.35%, 99.73%, and 97.89% for the lung, pulmonary lobe, and GGO segmentation, respectively. The experimental investigations on DeepChestNet-Lung, DeepChestNet-Lobe and DeepChestNet-COVID datasets, and comparison with several state-of-the-art approaches reveal the great potential of DeepChestNet for diagnosis of COVID-19 disease.