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KMID : 1137820230440020118
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의공학회지
2023 Volume.44 No. 2 p.118 ~ p.124
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A Performance Comparison Study of Lesion Detection Model according to Gastroscopy Image Quality
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Lee Yul-Hee
Kim Suh-Young
Kim Kwang-Gi
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Abstract
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Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gas- troscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high qual- ity data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addi- tion, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.
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KEYWORD
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Gastroscopy, Image quality assessment algorithm, Deep learning, RetinaNet, Lesion detection
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