RGB-to-hyperspectral conversion for accessible melanoma detection: A CNN-based approach

Abstract

One major difficulty facing the healthcare industry is the early and precise detection of melanoma. With its capacity to record a broad spectrum of electromagnetic wavelengths, hyperspectral imaging (HSI) is a promising technique for accurate melanoma diagnosis. However, the limited accessibility of HSI technology prevents it from being widely used. This article introduces a novel method for converting readily available red green blue (RGB) images to their hyperspectral counterparts using convolutional neural networks (CNNs). Through this conversion process, spectral information is improved, enabling a more thorough examination to detect melanoma. Principal component analysis (PCA) aids the machine learning algorithm in differentiating between melanoma and healthy moles in the classification model. Accuracy is significantly increased when spot detection and PCA are combined; Naïve Bayes achieves 76% accuracy in this way. These models are used in the developed web-based program SkinScan for real-time melanoma analysis, providing a useful and accessible solution. This work emphasizes how CNN-driven RGB-to-HSI conversion might improve melanoma detection accuracy and accessibility.

Keywords

• hyperspectral imaging
• melanoma
• neural networks
• deep-learning
• machine learning
• confusion matrix
• principal component analysis