Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy
Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : Tatwadarshi P. Nagarhalli, Shruti S. Pawar, Soham A. Dahanukar, Uday Aswalekar, Ashwini M. Save, Sanket D. Patil | ||
| DOI : 10.14445/22315381/IJETT-V73I6P106 | ||
How to Cite?
Tatwadarshi P. Nagarhalli, Shruti S. Pawar, Soham A. Dahanukar, Uday Aswalekar, Ashwini M. Save, Sanket D. Patil, "Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.52-64, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P106
Abstract
Accurately classifying white blood cells from microscopic images is essential to identify several illnesses and conditions in medical diagnostics. Many deep learning technologies are being employed to quickly and automatically classify images. However, most of the time, the resolution of these microscopic pictures is quite low, which might make it difficult to classify them correctly. Some picture improvement techniques, such as image super-resolution, are being utilized to improve the resolution of the photos to get around this issue. The suggested study uses large image dimension upscaling to investigate how picture-enhancing approaches affect classification performance. The study specifically looks at how deep learning models may be able to understand more complex visual information by capturing subtler morphological changes when image resolution is increased using cutting-edge techniques. The model may learn from standard and augmented data since the improved images are incorporated into the training process. This dual method seeks to comprehend the impact of image resolution on model performance and enhance classification accuracy. A well-known model for picture categorization is used to conduct extensive testing and thoroughly evaluate the effectiveness of this approach. This research intends to create more efficient image identification algorithms customized to a particular dataset of white blood cells by understanding the trade-offs between ordinary and enhanced images.
Keywords
Blood cell classification, Image super resolution, ESRGAN, Convolutional Neural Network, ResNet50, White blood cell classification
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