The Effect of Using Augmented Image in the Identification of Human Nail Abnormality using Yolo3

R. Pellegrino, Jethro Hoyt T. Lacuesta, Carl Ferione L. Dela Cuesta
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Abstract

Human-nail abnormality manifests the status of nail’s health and human health in general. Terry’s nail is common to people with severe liver disease. Spoon nail can be found in people with diabetes and heart diseases. High cholesterol causes the Splinter Hemorrhage abnormality in nail. Although studies on Human-nail have been developed, there is still a lack of datasets to further the study on nail to serve as an additional tool for diagnostic purposes on specific abnormalities: Splinter Hemorrhages, Terry's nail, and Spoon nail. This study aims to determine the effect of using augmented images in training and testing nail image dataset to identify nail abnormality. The study compares three models: an unaugmented model, an on-the-fly model, and a manually augmented model using the open-source python image augmentation library imgaug, to identify Splinter Hemorrhage, Terry's nail, and Spoon nail abnormalities with its associated diseases using Yolov3 on a Raspberry Pi 4 model B with libraries like OpenCV, Keras, and TensorFlow. The manually augmented model achieved the highest accuracy of 91% which is 5.58% higher than the on-the-fly model and 13.92% higher accuracy than the unaugmented model..
增强图像在Yolo3人体指甲异常识别中的应用
人甲异常是指甲健康状况和人体健康状况的综合体现。特里的指甲在患有严重肝病的人身上很常见。患有糖尿病和心脏病的人也会有匙状指甲。高胆固醇导致指甲裂出血异常。虽然对人类指甲的研究已经发展起来,但仍然缺乏数据集来进一步研究指甲,以作为特定异常诊断目的的额外工具:Splinter Hemorrhages, Terry's nail和Spoon nail。本研究旨在确定在训练和测试指甲图像数据集中使用增强图像来识别指甲异常的效果。该研究比较了三种模型:未增强模型,动态模型和使用开源python图像增强库imagogg的手动增强模型,以识别Splinter Hemorrhage, Terry's nail和Spoon nail异常及其相关疾病,使用Yolov3在Raspberry Pi 4模型B上使用OpenCV, Keras和TensorFlow等库。人工增强模型的精度最高,达到91%,比实时模型高5.58%,比未增强模型高13.92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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