tetano
Editor, Senior Moderator
Comput Biol Med
. 2023 Nov 30:168:107789.
doi: 10.1016/j.compbiomed.2023.107789. Online ahead of print. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture
Md Alamin Talukder[SUP] 1 [/SUP], Md Abu Layek[SUP] 2 [/SUP], Mohsin Kazi[SUP] 3 [/SUP], Md Ashraf Uddin[SUP] 4 [/SUP], Sunil Aryal[SUP] 5 [/SUP]
Affiliations
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.
Keywords: COVID-19; Deep learning; Diagnosis; EffiecientNet; Lung; Transfer learning; X-ray image.
. 2023 Nov 30:168:107789.
doi: 10.1016/j.compbiomed.2023.107789. Online ahead of print. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture
Md Alamin Talukder[SUP] 1 [/SUP], Md Abu Layek[SUP] 2 [/SUP], Mohsin Kazi[SUP] 3 [/SUP], Md Ashraf Uddin[SUP] 4 [/SUP], Sunil Aryal[SUP] 5 [/SUP]
Affiliations
- PMID: 38042105
- DOI: 10.1016/j.compbiomed.2023.107789
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.
Keywords: COVID-19; Deep learning; Diagnosis; EffiecientNet; Lung; Transfer learning; X-ray image.