Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation

作者：Jianhua Zhao, Harvey Lui, Sunil Kalia, Tim K. Lee, Haishan Zeng · 发表于：Frontiers in Oncology · 年份：2024 · DOI：10.3389/fonc.2024.1320220 · 被引用次数：23 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Cutaneous Melanoma Detection and Management、Spectroscopy and Chemometric Analyses

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the s...