利用深度学习技术从显微真菌图像中检测真菌感染

Ilkay Cinar, Yavuz Selim Taspinar
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引用次数: 0

摘要

真菌感染由于其多样的表现和不同的特征,在医学诊断中提出了重大挑战。本研究深入研究了应用深度学习技术从显微镜真菌图像中检测真菌感染。通过利用卷积神经网络(cnn)的力量,我们提出了一种使用迁移学习来准确分类不同真菌物种的方法。该数据集包括各种真菌类型的显微图像,为了提高模型性能,我们利用数据增强技术。此外,我们的目标是通过微调模型的层来提高性能。我们的实验结果从84.38%的准确率开始,逐步达到95.35%和97.19%的高值。这些结果强调了我们的深度学习方法在精确识别和分类真菌感染方面的有效性。这一成功有望帮助医疗专业人员及时准确地进行诊断。本研究的发现有助于正在进行的医学图像分析研究,并推动自动化疾病检测领域的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Fungal Infections from Microscopic Fungal Images Using Deep Learning Techniques
Fungal infections, due to their diverse manifestations and varying characteristics, present significant challenges in medical diagnosis. This study delves into applying deep-learning techniques for detecting fungal infections from microscopic fungal images. By harnessing the power of Convolutional Neural Networks (CNNs), we propose an approach that employs transfer learning to accurately classify different fungal species. The dataset comprises microscopic images of various fungal types, and to enhance model performance, we utilize data augmentation techniques. Furthermore, we aim to boost performance by fine-tuning the model's layers. Initially starting at 84.38% accuracy, our experimental results progressively reached high values of 95.35% and 97.19%. These results underscore the effectiveness of our deep learning approach in precisely identifying and classifying fungal infections. This success holds promising potential to aid medical professionals in timely and accurate diagnoses. The findings presented in this study contribute to ongoing research in medical image analysis and drive advancements in the field of automated disease detection.
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