ReviewSN computer science2023
Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.
Review in SN computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Achieving a New Artificial Intelligence System for Serum Protein Electrophoresis to Recognize M-Spikes.ACS omega · 2025Article
- Article
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
- Article
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Authors and funding
3 authors.
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Abstract
The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19.
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Registered trials
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