{"title":"图像因素对眼科超声深度学习模型性能的影响","authors":"","doi":"10.1016/j.irbm.2024.100848","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>This study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis.</p></div><div><h3>Methods</h3><p>A total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model.</p></div><div><h3>Results</h3><p>Inceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P > 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P < 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06).</p></div><div><h3>Conclusion</h3><p>These results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000290/pdfft?md5=d2db3fd118a09a6347da8a8332f055bd&pid=1-s2.0-S1959031824000290-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Influence of Image Factors on the Performance of Ophthalmic Ultrasound Deep Learning Model\",\"authors\":\"\",\"doi\":\"10.1016/j.irbm.2024.100848\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>This study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis.</p></div><div><h3>Methods</h3><p>A total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model.</p></div><div><h3>Results</h3><p>Inceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P > 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P < 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06).</p></div><div><h3>Conclusion</h3><p>These results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.</p></div>\",\"PeriodicalId\":14605,\"journal\":{\"name\":\"Irbm\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1959031824000290/pdfft?md5=d2db3fd118a09a6347da8a8332f055bd&pid=1-s2.0-S1959031824000290-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Irbm\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1959031824000290\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031824000290","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
本研究旨在评估图像因素对用于眼科超声图像诊断的深度学习模型性能的影响。本研究共使用了 3,373 幅眼科超声图像，以深入评估图像因素对深度学习分类模型性能的影响。使用 Inceptionv3、Xception 和融合模型 Inceptionv3-Xception 探索亮度、对比度、增益、噪声、大小、格式、伪彩色七个图像相关因素如何影响模型的分类性能。Inceptionv3-Xception 在各种图像因素的识别准确率方面具有优势。当图像亮度发生变化时，模型的性能呈下降趋势（0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05）。当图像对比度发生变化时，模型的性能相当（0.5 vs. 1 vs. 1.2，ACC 96.23 vs. 96.95 vs. 97.45，P > 0.05）。当图像增益下降到 50 dB 时，模型的准确性显著下降（50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05）。当图像中加入高斯噪声时，模型的性能逐渐下降（0.02 vs. 0, ACC 89.48vs97.06, P < 0.05）。当图像大小下降到原始图像的 25% 时，模型的性能显著下降（25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01）。当图像格式发生变化时，模型的识别准确率相似（JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05）。与灰度图像相比，模型识别伪彩色图像的准确率明显下降（灰度 vs. 伪彩色，ACC 35.96 vs. 97.06）。这些结果表明，图像质量在很大程度上影响着模型的训练过程，而获取高质量的图像是模型获得高识别性能的重要前提。这项研究为改进眼科超声图像识别的其他鲁棒深度学习模型提供了宝贵的启示。
Influence of Image Factors on the Performance of Ophthalmic Ultrasound Deep Learning Model
Objective
This study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis.
Methods
A total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model.
Results
Inceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P > 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P < 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06).
Conclusion
These results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.
期刊介绍：
IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux).
As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in:
-Physiological and Biological Signal processing (EEG, MEG, ECG…)-
Medical Image processing-
Biomechanics-
Biomaterials-
Medical Physics-
Biophysics-
Physiological and Biological Sensors-
Information technologies in healthcare-
Disability research-
Computational physiology-
…
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