Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy

TL;DR

This study investigates how image super-resolution techniques influence the accuracy of white blood cell classification using deep learning, aiming to improve diagnostic performance by enhancing image quality.

Contribution

It introduces a dual approach combining standard and super-resolved images in training to analyze their impact on classification accuracy.

Findings

Super-resolution improves classification accuracy.

Enhanced images help models learn finer morphological details.

The approach optimizes image-based diagnostic algorithms.

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.