Simple machine learning methods work surprisingly well for Ramanomics

Recently, a deep convolutional neural network was employed to detect liver cancer by Ramanomics. Results on a demo dataset claimed an accuracy of about 90% to be achieved after around an hour of training in a modern desktop. However, my experience with another Ramanomics dataset taught me that simple methods could potentially outperform deep learning. Here, I tested the simple and interpretable method of logistic regression. It achieved an accuracy of around 90.4% in under a minute. Employing a random decision forest, yields an accuracy of 92.6% in under 10 seconds. Thus, although deep learning is promising, it is yet to provide a quantum-leap in performance for Ramanomics. A biophysics aware machine learning method would be more welcome!