基于卷积神经网络的多类皮肤癌图像分类

J. Ramya, H. Vijaylakshmi, H. Saifuddin
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引用次数: 0

摘要

皮肤癌是最危险的,在全世界有三分之一的确诊率。如果在开始阶段没有被发现,它应该会扩散到身体的其他部位。皮肤癌诊断系统包括图像分割、特征提取和分类三个主要步骤。首先用二维中值滤波对图像进行预处理,去除毛发、凝胶、气泡等伪影,然后用离散小波变换对图像进行分割,从背景皮肤区域中提取病灶区域。在本文中,我们将之前分割的图像传递给卷积神经网络(CNN)进行特征学习和分类。在三类PH2数据集上进行了实验,分类结果令人满意且准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network For Multiclass Skin Cancer Image Classification
Skin cancer is the most dangerous, having one-third diagnosed rate in worldwide. It is supposed to spread vastly to other body parts when it is not detected at beginning stage. Skin cancer diagnosing system contains three major steps such as segmentation, feature extraction and classification. Firstly, images are pre-processed using 2D median filter to remove some artifacts like thin hair, gel, air bubble etc. followed by segmentation using discrete wavelet transform to extract lesion region from background skin region is described in our last paper. In this paper, we have passed previously segmented images to Convolutional Neural Network (CNN) for feature learning and classification. Experimentation conducted on three classes of PH2 dataset, classification results are satisfactory and accurate.
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