使用基于非局部手段去噪和稀疏字典学习的 CNN 进行皮肤癌分类

Apeksha Pandey, Manepalli Sai Teja, Parul Sahare, Vipin Kamble, Mayur Parate, Mohammad Farukh Hashmi
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引用次数: 0

摘要

当今世界,皮肤病越来越普遍。随着皮肤病的增多,需要对患者皮肤完全无创的计算机化技术。因此,深度学习模型已成为计算机检测皮肤病的标准。这些模型的性能效率随着获取更多数据而提高,其主要目的是进行图像分类。在本文中,我们介绍了一种使用图像处理技术、非局部手段去噪和卷积神经网络(CNN)并辅以稀疏字典学习的皮肤病检测方法。在这里,在图像分类中使用 NLM 去噪和稀疏字典学习与 CNN 的主要好处在于利用多阶段方法提高输入数据的质量,提取有意义和有区分度的特征,并提高分类模型的整体性能。这种组合方法可以解决噪声鲁棒性、特征提取和分类准确性等难题,因此在复杂的图像分析任务中尤为有效。在去噪方面,HAM-10000 数据集图像的平均峰值信噪比(PSNR)为 33.59 dB。对于 ISIC-2019 数据集,训练文件夹的平均 PSNR 为 34.37 dB,测试文件夹的平均 PSNR 为 34.39 dB。使用 CNN 模型训练的深度学习网络用于分析皮肤癌图像,在皮肤癌类型分类方面取得了可接受的结果。使用的数据集包含高分辨率图像。经过所有测试,HAM-10000 数据集的准确率为 85.61%,ISIC-2019 数据集的准确率为 81.23%,与基准测试结果验证的现有方法相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skin cancer classification using non-local means denoising and sparse dictionary learning based CNN
Skin conditions are becoming increasingly prevalent across the world in current times. With the rise in dermatological disorders, there is a need for computerized techniques that are completely noninvasive to patients’ skin. As a result, deep learning models have become standard for the computerized detection of skin diseases. The performance efficiency of these models improves with access to more data with their primary aim being image classification. In this paper, we present a skin disease detection methodology using image processing techniques, non-local means denoising and convolutional neural network (CNN) backed by sparse dictionary learning. Here, the major benefit of using NLM denoising followed by sparse dictionary learning with CNNs in image classification lies in leveraging a multi-stage approach that enhances the quality of input data, extracts meaningful and discriminative features, and improves the overall performance of the classification model. This combined approach addresses challenges such as noise robustness, feature extraction, and classification accuracy, making it particularly effective in complex image analysis tasks. For denoising, the average Peak Signal to Noise Ratio (PSNR) obtained for images from HAM-10000 dataset is 33.59 dB. For the ISIC-2019 dataset, the average PSNR for the train folder is 34.37 dB, and for the test folder it is 34.39 dB. The deep learning network is trained for the analysis of skin cancer images using a CNN model and is achieving acceptable results in classifying skin cancer types. The datasets used contain high-resolution images. After all the tests, the accuracy obtained is 85.61% for the HAM-10000 dataset and 81.23% for the ISIC-2019 dataset, which is on par with existing approaches validated by benchmarking results.
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