Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images

S. Simic, S. Simic, Z. Bankovic, M. Ivkov-Simic, J. Villar, D. Simić
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引用次数: 2

Abstract

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.
深度卷积神经网络在皮肤肿瘤图像自动分类中的应用
皮肤是人体与外界的独特接口,在人体生活中起着多方面的免疫作用。在医疗实践中,早期准确发现所有类型的皮肤肿瘤对于指导适当的管理和提高患者的生存率至关重要。最重要的问题是区分恶性皮肤肿瘤和良性病变。本研究的目的是通过分析医学皮肤肿瘤皮肤镜图像对皮肤肿瘤进行分类。本文研究了一种基于深度卷积神经网络的新策略,该策略在皮肤肿瘤图像的自动分类中表现出最新的性能。该系统在知名的HAM10000数据集上进行了测试。对实验结果进行了验证,并与同类研究结果进行了对比。
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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