HCUGAN: Hybrid Cyclic UNET GAN for Generating Augmented Synthetic Images of Chest X-Ray Images for Multi Classification of Lung Diseases
How to Cite?
Vrinda. K, Dhanesh G. Kurup, "A Broadband Equivalent Model of On-Chip Spiral Inductors using Differential Evolution Algorithm," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 249-253, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P227
In 2019, when COVID-19 started spreading as a virally infection across the globe, people started researching using the available chest x-ray images of lungs. But during the detection process, the research article found that the available images are less and most of the symptoms are similar to pneumonia disease. So in this article, before performing the multi-classification process, tries to enhance the size of the dataset by combining the UNET with cyclic GAN’s. Most of the real-world data exist in an imbalanced form, which affects the overall architecture and performance of the design. Many of the researchers have implemented manipulation techniques like translation, rotation, and others for enhancement of dataset size, but due to the high dimensionality of the medical images, these basic and simple approaches doesn’t have any impact on the model. So, the improved cyclic GAN’s mechanism helps the research article to create a balanced dataset with more augmented or reconstructed CXR images by performing segmentation using the UNET operation.
Cyclic GAN, Semantic Segmentation, UNET’s, Up-Down sampling, Cycle Consistency, Contraction and Expansion Path.
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