Maurya Ritesh, Mahapatra Satyajit, Dutta Malay Kishore, Singh Vibhav Prakash, Karnati Mohan, Sahu Geet, Pandey Nageshwar Nath
Amity Centre for Artificial Intelligence, Amity University, Noida, India.
Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Sci Rep. 2024 Aug 1;14(1):17785. doi: 10.1038/s41598-024-68749-1.
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.
皮肤癌是一种致命疾病，其早期检测对于防止癌细胞扩散至身体其他器官和组织起着关键作用。基于人工智能（AI）的自动化方法在皮肤癌早期检测中可发挥重要作用。本研究提出了一种基于AI的新颖方法，称为“DualAutoELM”，用于有效识别各类皮肤癌。该方法利用了一个由两个不同的自动编码器组成的自动编码器网络：空间自动编码器和快速傅里叶变换（FFT）自动编码器。空间自动编码器专门用于学习输入病变图像中的空间特征，而FFT自动编码器则通过重建过程学习捕捉变换后的输入皮肤病变图像中的纹理和显著频率模式。在这些自动编码器的编码器部分的各个层级使用注意力模块，显著提高了它们的判别特征学习能力。使用具有单层前馈的极限学习机（ELM），根据从这些自动编码器的瓶颈层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，用于全面评估所提出的方法。实验结果证明了所提技术的准确性和稳健性，“HAM10000”数据集的AUC、精确率和准确率分别为0.98、97.68%和97.66%，“ISIC - 2017”数据集的相应值分别为0.95、86.75%和86.68%。本研究突出了所提方法准确检测皮肤癌的可能性。
