Nanofabrication in polymeric materials with Raman scattering techniques based on noninvasive imaging for tumor precursor lesions

Raman Spectroscopy has long been expected to aid in clinical decision-making, particularly in the categorization of oncological materials. The intricacy of Raman data has, however, limited its use in therapeutic settings. While conventional machine learning models have made use of this data, new advances in deep learning show promise for furthering the area. In this research, we offer a new machine learning-based technique for detecting tumour precursor lesions in polymeric materials using Raman scattering and nanofabrication. In this case, a spectral analysis based on Raman scattering light intensity was applied to the input tumour picture. The precursor lesion is then elevated using perceptron component analysis using a Kernelization-based convolutional regression. Several skin cancer datasets are analysed experimentally in terms of the F-1 score, area under the ROC curve (AUC), mean squared error (MSE), and precision throughout the training and validation phases. Raman spectral fingerprinting provides an inherent "molecular fingerprint" of a tissue that reflects any biochemical change associated with an inflammatory or malignant tissue state. The proposed method achieved a 95% accuracy in training, a 96% accuracy in validation, a 92% precision, an F-1 score of 90%, an area under the curve (AUC) of 68%, and a MSE of 63%.