从各种类型的痘中检测猴痘:一种深度学习方法

Anik Pramanik, Fayazunnesa Chowdhury, Salma Sultana, Md. Mahbubur Rahman, Md. Hasan Imam Bijoy, Md. Sadekur Rahman
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引用次数: 0

摘要

根据世界卫生组织(WHO)的统计,迄今为止,猴痘已在127个国家被确定为流行病,并在全球迅速蔓延。而与猴痘相关的皮疹和皮肤损伤通常与水痘和麻疹等其他痘相似。由于这些相似之处,医学专业人员仅根据病变和皮疹的外观来识别猴痘可能具有挑战性。由于猴痘在本次暴发之前并不常见,卫生保健专业人员缺乏这方面的知识。但是,由于图像处理方法在COVID-19检测中的成功,科学界对在数字皮肤图像中应用人工智能进行猴痘预测和检测越来越感兴趣。在这项研究中,我们应用了三个前沿的深度学习模型,即InceptionV3, MobileNetV3和DenseNet201,称为迁移学习模型,使用公开可用的猴痘皮肤图像数据集2022(包含四个类)检测皮肤图像上的猴痘。根据我们的研究,迁移学习模型可以在数字化皮肤图像上检测猴痘,在三种实现算法中,InceptionNet-V3预训练模型的准确率最高为93.59%。为了进一步的研究,需要更大的训练图像来训练这些深度学习模型,以达到更高的有力检测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monkeypox Detection from Various Types of Poxes: A Deep Learning Approach
According to World Health Organization (WHO) statistics, monkeypox has been identified as an epidemic in 127 nations so far, and it is spreading quickly over the globe. While the rashes and skin lesions associated with monkeypox usually mimic those of other poxes, including chickenpox and measles. Due to these similarities, it could be challenging for medical professionals to identify monkeypox based just on the appearance of lesions and rashes. Because monkeypox was uncommon before in the current outbreak, healthcare professionals lack knowledge in this area. But the scientific community has demonstrated a rising interest in implementing Artificial Intelligence in Monkeypox prediction and detection from digital skin images as a result of the success of image processing approaches in COVID-19 detection. In this study, we have applied three cutting-edge deep learning models which are InceptionV3, MobileNetV3, and DenseNet201, referred to as transfer learning models, to detect monkeypox on skin images using the publicly available Monkeypox Skin Image Dataset 2022 with four classes. According to our research, transfer learning models can detect monkeypox with a top 93.59% accuracy for the InceptionNet-V3 pre-trained model from three implemented algorithms on digitized skin images. For further research, larger training images are required to train those deep learning models to achieve a higher vigorous detection rate.
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