PDF(Pubmed)
Abstract：
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed \'DualAutoELM\' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The \'HAM10000\' and \'ISIC-2017\' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the \'HAM10000\' dataset being 0.98, 97.68% and 97.66%, and for the \'ISIC-2017\' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.
摘要：
皮肤癌是一种致命的疾病,它的早期检测在防止其传播到其他身体器官和组织中起着关键作用。基于人工智能（AI）的自动化方法可以在其早期检测中发挥重要作用。这项研究提出了一种基于人工智能的新方法，被称为“DualAutoELM”，用于有效识别各种类型的皮肤癌。所提出的方法利用了自动编码器网络，包括两个不同的自动编码器:空间自动编码器和FFT(快速傅里叶变换)自动编码器。空间自动编码器专门学习输入病变图像内的空间特征,而FFT自动编码器通过重建过程学习捕获经变换的输入皮肤病变图像内的纹理和区分频率模式。在这些自动编码器的编码器部分内的各个级别处使用注意力模块显著地提高了它们的辨别特征学习能力。训练具有单层前馈的极限学习机（ELM），以使用从这些自动编码器的瓶颈层中恢复的特征对皮肤恶性肿瘤进行分类。“HAM10000”和“ISIC-2017”是两个公开可用的数据集，用于彻底评估建议的方法。实验结果证明了该技术的准确性和鲁棒性。AUC,精度,“HAM10000”数据集的精度值为0.98、97.68%和97.66%，对于“ISIC-2017”数据集，分别为0.95、86.75%和86.68%，分别。这项研究强调了准确检测皮肤癌的建议方法的可能性。
